Overview

Dataset statistics

Number of variables86
Number of observations1038503
Missing cells14794086
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory681.4 MiB
Average record size in memory688.0 B

Variable types

Categorical69
Numeric15
Boolean2

Alerts

Date1 of Occurrence has constant value ""Constant
Date2 of Occurrence has constant value ""Constant
Family Offense has constant value ""Constant
Incident Number w/year has a high cardinality: 884641 distinct valuesHigh cardinality
Service Number ID has a high cardinality: 1038503 distinct valuesHigh cardinality
Call (911) Problem has a high cardinality: 124 distinct valuesHigh cardinality
Type of Incident has a high cardinality: 1103 distinct valuesHigh cardinality
Type Location has a high cardinality: 73 distinct valuesHigh cardinality
Incident Address has a high cardinality: 195888 distinct valuesHigh cardinality
Apartment Number has a high cardinality: 13956 distinct valuesHigh cardinality
Time1 of Occurrence has a high cardinality: 1440 distinct valuesHigh cardinality
Time2 of Occurrence has a high cardinality: 1440 distinct valuesHigh cardinality
Date of Report has a high cardinality: 3600 distinct valuesHigh cardinality
Date incident created has a high cardinality: 3600 distinct valuesHigh cardinality
Offense Entered Time has a high cardinality: 1440 distinct valuesHigh cardinality
CFS Number has a high cardinality: 795822 distinct valuesHigh cardinality
Call Received Date Time has a high cardinality: 3600 distinct valuesHigh cardinality
Call Date Time has a high cardinality: 3600 distinct valuesHigh cardinality
Call Cleared Date Time has a high cardinality: 3600 distinct valuesHigh cardinality
Call Dispatch Date Time has a high cardinality: 3600 distinct valuesHigh cardinality
Responding Officer #1 Badge No has a high cardinality: 4642 distinct valuesHigh cardinality
Responding Officer #1 Name has a high cardinality: 4579 distinct valuesHigh cardinality
Responding Officer #2 Badge No has a high cardinality: 4644 distinct valuesHigh cardinality
Responding Officer #2 Name has a high cardinality: 4620 distinct valuesHigh cardinality
Reporting Officer Badge No has a high cardinality: 4700 distinct valuesHigh cardinality
Assisting Officer Badge No has a high cardinality: 2077 distinct valuesHigh cardinality
Reviewing Officer Badge No has a high cardinality: 255 distinct valuesHigh cardinality
Element Number Assigned has a high cardinality: 4560 distinct valuesHigh cardinality
Investigating Unit 2 has a high cardinality: 58 distinct valuesHigh cardinality
Modus Operandi (MO) has a high cardinality: 510789 distinct valuesHigh cardinality
RMS Code has a high cardinality: 1291 distinct valuesHigh cardinality
Penal Code has a high cardinality: 607 distinct valuesHigh cardinality
UCR Offense Name has a high cardinality: 51 distinct valuesHigh cardinality
NIBRS Crime has a high cardinality: 54 distinct valuesHigh cardinality
Update Date has a high cardinality: 3600 distinct valuesHigh cardinality
City has a high cardinality: 147 distinct valuesHigh cardinality
Location1 has a high cardinality: 244593 distinct valuesHigh cardinality
Year of Incident is highly overall correlated with Year1 of Occurrence and 3 other fieldsHigh correlation
Reporting Area is highly overall correlated with Division and 3 other fieldsHigh correlation
Beat is highly overall correlated with Sector and 5 other fieldsHigh correlation
Sector is highly overall correlated with Beat and 5 other fieldsHigh correlation
Year1 of Occurrence is highly overall correlated with Year of Incident and 3 other fieldsHigh correlation
Day1 of the Year is highly overall correlated with Day2 of the Year and 4 other fieldsHigh correlation
Year2 of Occurrence is highly overall correlated with Year of Incident and 4 other fieldsHigh correlation
Day2 of the Year is highly overall correlated with Day1 of the Year and 4 other fieldsHigh correlation
Offense Entered Year is highly overall correlated with Year of Incident and 3 other fieldsHigh correlation
Offense Entered Date/Time is highly overall correlated with Day1 of the Year and 4 other fieldsHigh correlation
Criminal Justice Information Service Code is highly overall correlated with UCR Code and 8 other fieldsHigh correlation
UCR Code is highly overall correlated with Criminal Justice Information Service Code and 11 other fieldsHigh correlation
X Coordinate is highly overall correlated with Council District and 2 other fieldsHigh correlation
Y Cordinate is highly overall correlated with Council District and 2 other fieldsHigh correlation
Zip Code is highly overall correlated with Type of Property and 4 other fieldsHigh correlation
Type Location is highly overall correlated with Type of PropertyHigh correlation
Type of Property is highly overall correlated with Zip Code and 1 other fieldsHigh correlation
Division is highly overall correlated with Reporting Area and 5 other fieldsHigh correlation
Council District is highly overall correlated with Reporting Area and 7 other fieldsHigh correlation
Target Area Action Grids is highly overall correlated with Reporting Area and 7 other fieldsHigh correlation
Community is highly overall correlated with Reporting Area and 8 other fieldsHigh correlation
Month1 of Occurence is highly overall correlated with Day1 of the Year and 4 other fieldsHigh correlation
Day1 of the Week is highly overall correlated with Day2 of the Week and 1 other fieldsHigh correlation
Month2 of Occurence is highly overall correlated with Day1 of the Year and 4 other fieldsHigh correlation
Day2 of the Week is highly overall correlated with Day1 of the Week and 1 other fieldsHigh correlation
Offense Entered Month is highly overall correlated with Day1 of the Year and 4 other fieldsHigh correlation
Offense Entered Day of the Week is highly overall correlated with Day1 of the Week and 1 other fieldsHigh correlation
Special Report (Pre-RMS) is highly overall correlated with Year2 of Occurrence and 2 other fieldsHigh correlation
Victim Race is highly overall correlated with Victim EthnicityHigh correlation
Victim Ethnicity is highly overall correlated with Victim RaceHigh correlation
Investigating Unit 1 is highly overall correlated with UCR Code and 3 other fieldsHigh correlation
Investigating Unit 2 is highly overall correlated with Beat and 6 other fieldsHigh correlation
Offense Status is highly overall correlated with UCR Disposition and 1 other fieldsHigh correlation
UCR Disposition is highly overall correlated with Offense StatusHigh correlation
Hate Crime is highly overall correlated with Year of Incident and 6 other fieldsHigh correlation
Weapon Used is highly overall correlated with Hate CrimeHigh correlation
Gang Related Offense is highly overall correlated with Hate CrimeHigh correlation
Drug Related Istevencident is highly overall correlated with NIBRS Crime and 2 other fieldsHigh correlation
UCR Offense Name is highly overall correlated with Criminal Justice Information Service Code and 11 other fieldsHigh correlation
UCR Offense Description is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
Offense Type is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
NIBRS Crime is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
NIBRS Crime Category is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
NIBRS Crime Against is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
NIBRS Code is highly overall correlated with Criminal Justice Information Service Code and 10 other fieldsHigh correlation
NIBRS Group is highly overall correlated with Criminal Justice Information Service Code and 11 other fieldsHigh correlation
NIBRS Type is highly overall correlated with UCR Code and 8 other fieldsHigh correlation
State is highly overall correlated with Zip CodeHigh correlation
Person Involvement Type is highly imbalanced (90.3%)Imbalance
Victim Type is highly imbalanced (54.1%)Imbalance
Victim Gender is highly imbalanced (56.2%)Imbalance
Investigating Unit 1 is highly imbalanced (78.7%)Imbalance
Offense Status is highly imbalanced (68.1%)Imbalance
UCR Disposition is highly imbalanced (71.9%)Imbalance
Hate Crime Description is highly imbalanced (96.4%)Imbalance
Weapon Used is highly imbalanced (58.5%)Imbalance
Gang Related Offense is highly imbalanced (68.1%)Imbalance
Drug Related Istevencident is highly imbalanced (74.7%)Imbalance
City is highly imbalanced (99.0%)Imbalance
State is highly imbalanced (99.7%)Imbalance
Call (911) Problem has 58418 (5.6%) missing valuesMissing
Type of Property has 809505 (77.9%) missing valuesMissing
Apartment Number has 804068 (77.4%) missing valuesMissing
Target Area Action Grids has 661759 (63.7%) missing valuesMissing
Community has 924457 (89.0%) missing valuesMissing
CFS Number has 58417 (5.6%) missing valuesMissing
Call Received Date Time has 58417 (5.6%) missing valuesMissing
Call Date Time has 58417 (5.6%) missing valuesMissing
Call Cleared Date Time has 58884 (5.7%) missing valuesMissing
Call Dispatch Date Time has 58592 (5.6%) missing valuesMissing
Special Report (Pre-RMS) has 1034297 (99.6%) missing valuesMissing
Person Involvement Type has 38053 (3.7%) missing valuesMissing
Victim Type has 45799 (4.4%) missing valuesMissing
Victim Race has 374754 (36.1%) missing valuesMissing
Victim Ethnicity has 375735 (36.2%) missing valuesMissing
Victim Gender has 377881 (36.4%) missing valuesMissing
Responding Officer #1 Badge No has 60790 (5.9%) missing valuesMissing
Responding Officer #1 Name has 61672 (5.9%) missing valuesMissing
Responding Officer #2 Badge No has 691363 (66.6%) missing valuesMissing
Responding Officer #2 Name has 691365 (66.6%) missing valuesMissing
Reporting Officer Badge No has 58567 (5.6%) missing valuesMissing
Assisting Officer Badge No has 248151 (23.9%) missing valuesMissing
Element Number Assigned has 59208 (5.7%) missing valuesMissing
Investigating Unit 1 has 267376 (25.7%) missing valuesMissing
Investigating Unit 2 has 267357 (25.7%) missing valuesMissing
Offense Status has 11338 (1.1%) missing valuesMissing
UCR Disposition has 11188 (1.1%) missing valuesMissing
Modus Operandi (MO) has 81448 (7.8%) missing valuesMissing
Family Offense has 57458 (5.5%) missing valuesMissing
Hate Crime has 1037343 (99.9%) missing valuesMissing
Weapon Used has 640948 (61.7%) missing valuesMissing
Gang Related Offense has 579035 (55.8%) missing valuesMissing
Drug Related Istevencident has 57549 (5.5%) missing valuesMissing
UCR Offense Name has 631134 (60.8%) missing valuesMissing
UCR Offense Description has 631133 (60.8%) missing valuesMissing
UCR Code has 631133 (60.8%) missing valuesMissing
Offense Type has 631133 (60.8%) missing valuesMissing
NIBRS Crime has 257029 (24.7%) missing valuesMissing
NIBRS Crime Category has 257029 (24.7%) missing valuesMissing
NIBRS Crime Against has 257029 (24.7%) missing valuesMissing
NIBRS Code has 257029 (24.7%) missing valuesMissing
NIBRS Group has 257029 (24.7%) missing valuesMissing
NIBRS Type has 257029 (24.7%) missing valuesMissing
State has 15296 (1.5%) missing valuesMissing
Zip Code is highly skewed (γ1 = -297.4925045)Skewed
Incident Number w/year is uniformly distributedUniform
Service Number ID is uniformly distributedUniform
CFS Number is uniformly distributedUniform
Service Number ID has unique valuesUnique

Reproduction

Analysis started2023-04-21 19:34:45.826632
Analysis finished2023-04-21 19:41:27.804829
Duration6 minutes and 41.98 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Incident Number w/year
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct884641
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
216100-2017
 
139
210974-2022
 
32
107406-2019
 
24
073944-2016
 
23
211119-2019
 
22
Other values (884636)
1038263 

Length

Max length13
Median length11
Mean length11.000192
Min length6

Characters and Unicode

Total characters11423732
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique752871 ?
Unique (%)72.5%

Sample

1st row203058-2022
2nd row232349-2022
3rd row217269-2022
4th row232572-2021
5th row223521-2022

Common Values

ValueCountFrequency (%)
216100-2017 139
 
< 0.1%
210974-2022 32
 
< 0.1%
107406-2019 24
 
< 0.1%
073944-2016 23
 
< 0.1%
211119-2019 22
 
< 0.1%
053209-2020 21
 
< 0.1%
199587-2014 21
 
< 0.1%
186195-2019 18
 
< 0.1%
246967-2018 18
 
< 0.1%
019483-2020 18
 
< 0.1%
Other values (884631) 1038167
> 99.9%

Length

2023-04-21T15:41:27.898110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
216100-2017 139
 
< 0.1%
210974-2022 32
 
< 0.1%
107406-2019 24
 
< 0.1%
073944-2016 23
 
< 0.1%
211119-2019 22
 
< 0.1%
053209-2020 21
 
< 0.1%
199587-2014 21
 
< 0.1%
186195-2019 18
 
< 0.1%
246967-2018 18
 
< 0.1%
019483-2020 18
 
< 0.1%
Other values (884634) 1038171
> 99.9%

Most occurring characters

ValueCountFrequency (%)
2 2382634
20.9%
0 2098801
18.4%
1 1673741
14.7%
- 1038285
9.1%
8 677083
 
5.9%
9 630522
 
5.5%
6 608278
 
5.3%
5 606298
 
5.3%
7 599429
 
5.2%
4 569276
 
5.0%
Other values (4) 539385
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10385433
90.9%
Dash Punctuation 1038285
 
9.1%
Uppercase Letter 8
 
< 0.1%
Space Separator 5
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2382634
22.9%
0 2098801
20.2%
1 1673741
16.1%
8 677083
 
6.5%
9 630522
 
6.1%
6 608278
 
5.9%
5 606298
 
5.8%
7 599429
 
5.8%
4 569276
 
5.5%
3 539371
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 1038285
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11423724
> 99.9%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2382634
20.9%
0 2098801
18.4%
1 1673741
14.7%
- 1038285
9.1%
8 677083
 
5.9%
9 630522
 
5.5%
6 608278
 
5.3%
5 606298
 
5.3%
7 599429
 
5.2%
4 569276
 
5.0%
Other values (3) 539377
 
4.7%
Latin
ValueCountFrequency (%)
B 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11423732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2382634
20.9%
0 2098801
18.4%
1 1673741
14.7%
- 1038285
9.1%
8 677083
 
5.9%
9 630522
 
5.5%
6 608278
 
5.3%
5 606298
 
5.3%
7 599429
 
5.2%
4 569276
 
5.0%
Other values (4) 539385
 
4.7%

Year of Incident
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5919
Minimum2010
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:27.968565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2014
Q12017
median2019
Q32021
95-th percentile2022
Maximum2024
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4841144
Coefficient of variation (CV)0.0012306174
Kurtosis-1.0767977
Mean2018.5919
Median Absolute Deviation (MAD)2
Skewness-0.21163159
Sum2.0963138 × 109
Variance6.1708245
MonotonicityNot monotonic
2023-04-21T15:41:28.024855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2022 139044
13.4%
2021 137933
13.3%
2020 134940
13.0%
2019 134281
12.9%
2018 124052
11.9%
2016 102761
9.9%
2017 98197
9.5%
2015 95974
9.2%
2014 58214
5.6%
2023 13063
 
1.3%
Other values (4) 44
 
< 0.1%
ValueCountFrequency (%)
2010 1
 
< 0.1%
2012 1
 
< 0.1%
2013 41
 
< 0.1%
2014 58214
5.6%
2015 95974
9.2%
2016 102761
9.9%
2017 98197
9.5%
2018 124052
11.9%
2019 134281
12.9%
2020 134940
13.0%
ValueCountFrequency (%)
2024 1
 
< 0.1%
2023 13063
 
1.3%
2022 139044
13.4%
2021 137933
13.3%
2020 134940
13.0%
2019 134281
12.9%
2018 124052
11.9%
2017 98197
9.5%
2016 102761
9.9%
2015 95974
9.2%

Service Number ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1038503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
203058-2022-01
 
1
021853-2019-01
 
1
204835-2016-01
 
1
079958-2017-01
 
1
249959-2014-01
 
1
Other values (1038498)
1038498 

Length

Max length16
Median length14
Mean length14.000233
Min length9

Characters and Unicode

Total characters14539284
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1038503 ?
Unique (%)100.0%

Sample

1st row203058-2022-01
2nd row232349-2022-01
3rd row217269-2022-01
4th row232572-2021-02
5th row223521-2022-01

Common Values

ValueCountFrequency (%)
203058-2022-01 1
 
< 0.1%
021853-2019-01 1
 
< 0.1%
204835-2016-01 1
 
< 0.1%
079958-2017-01 1
 
< 0.1%
249959-2014-01 1
 
< 0.1%
222235-2017-01 1
 
< 0.1%
204335-2014-01 1
 
< 0.1%
173343-2019-01 1
 
< 0.1%
007153-2016-01 1
 
< 0.1%
104857-2017-01 1
 
< 0.1%
Other values (1038493) 1038493
> 99.9%

Length

2023-04-21T15:41:28.137685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-01 2
 
< 0.1%
203058-2022-01 1
 
< 0.1%
056033-2020-01 1
 
< 0.1%
001389-2023-01 1
 
< 0.1%
229551-2022-02 1
 
< 0.1%
217269-2022-01 1
 
< 0.1%
232572-2021-02 1
 
< 0.1%
223521-2022-01 1
 
< 0.1%
225434-2022-01 1
 
< 0.1%
177540-2022-02 1
 
< 0.1%
Other values (1038496) 1038496
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 3136954
21.6%
1 2558817
17.6%
2 2514512
17.3%
- 2076788
14.3%
8 677323
 
4.7%
9 630680
 
4.3%
6 609010
 
4.2%
5 607864
 
4.2%
7 599788
 
4.1%
4 573639
 
3.9%
Other values (4) 553909
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12462482
85.7%
Dash Punctuation 2076788
 
14.3%
Uppercase Letter 8
 
< 0.1%
Space Separator 5
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3136954
25.2%
1 2558817
20.5%
2 2514512
20.2%
8 677323
 
5.4%
9 630680
 
5.1%
6 609010
 
4.9%
5 607864
 
4.9%
7 599788
 
4.8%
4 573639
 
4.6%
3 553895
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 2076788
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14539276
> 99.9%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3136954
21.6%
1 2558817
17.6%
2 2514512
17.3%
- 2076788
14.3%
8 677323
 
4.7%
9 630680
 
4.3%
6 609010
 
4.2%
5 607864
 
4.2%
7 599788
 
4.1%
4 573639
 
3.9%
Other values (3) 553901
 
3.8%
Latin
ValueCountFrequency (%)
B 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14539284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3136954
21.6%
1 2558817
17.6%
2 2514512
17.3%
- 2076788
14.3%
8 677323
 
4.7%
9 630680
 
4.3%
6 609010
 
4.2%
5 607864
 
4.2%
7 599788
 
4.1%
4 573639
 
3.9%
Other values (4) 553909
 
3.8%

Watch
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
1
414900 
3
320693 
2
302894 
U
 
15
2022
 
1

Length

Max length4
Median length1
Mean length1.0000029
Min length1

Characters and Unicode

Total characters1038506
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302894
29.2%
U 15
 
< 0.1%
2022 1
 
< 0.1%

Length

2023-04-21T15:41:28.215036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:28.295226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302894
29.2%
u 15
 
< 0.1%
2022 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302897
29.2%
U 15
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1038491
> 99.9%
Uppercase Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302897
29.2%
0 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
U 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1038491
> 99.9%
Latin 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302897
29.2%
0 1
 
< 0.1%
Latin
ValueCountFrequency (%)
U 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1038506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 414900
40.0%
3 320693
30.9%
2 302897
29.2%
U 15
 
< 0.1%
0 1
 
< 0.1%

Call (911) Problem
Categorical

HIGH CARDINALITY  MISSING 

Distinct124
Distinct (%)< 0.1%
Missing58418
Missing (%)5.6%
Memory size7.9 MiB
58 - ROUTINE INVESTIGATION
121030 
09V - UUMV
114541 
11V - BURG MOTOR VEH
75491 
6X - MAJOR DIST (VIOLENCE)
66727 
11R - BURG OF RES
 
46752
Other values (119)
555544 

Length

Max length30
Median length27
Mean length18.989468
Min length10

Characters and Unicode

Total characters18611293
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPSE/09 - THEFT
2nd row40/01 - OTHER
3rd row11B - BURG OF BUS
4th row6X - MAJOR DIST (VIOLENCE)
5th rowDASF-DIST ACTIVE SHOOTER FOOT

Common Values

ValueCountFrequency (%)
58 - ROUTINE INVESTIGATION 121030
 
11.7%
09V - UUMV 114541
 
11.0%
11V - BURG MOTOR VEH 75491
 
7.3%
6X - MAJOR DIST (VIOLENCE) 66727
 
6.4%
11R - BURG OF RES 46752
 
4.5%
40/01 - OTHER 42822
 
4.1%
09 - THEFT 34593
 
3.3%
40 - OTHER 33719
 
3.2%
PSE/09 - THEFT 31341
 
3.0%
31 - CRIMINAL MISCHIEF 30997
 
3.0%
Other values (114) 382072
36.8%
(Missing) 58418
 
5.6%

Length

2023-04-21T15:41:28.367416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
938285
23.9%
burg 192813
 
4.9%
uumv 129426
 
3.3%
58 121030
 
3.1%
routine 121030
 
3.1%
investigation 121030
 
3.1%
09v 114541
 
2.9%
veh 109614
 
2.8%
motor 102971
 
2.6%
major 96220
 
2.4%
Other values (275) 1884296
47.9%

Most occurring characters

ValueCountFrequency (%)
2961532
15.9%
E 1214966
 
6.5%
R 1143991
 
6.1%
O 1105541
 
5.9%
I 1071746
 
5.8%
T 1049779
 
5.6%
- 980962
 
5.3%
N 755746
 
4.1%
U 708805
 
3.8%
V 681981
 
3.7%
Other values (36) 6936244
37.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12339738
66.3%
Space Separator 2961532
 
15.9%
Decimal Number 1991164
 
10.7%
Dash Punctuation 980962
 
5.3%
Other Punctuation 197707
 
1.1%
Close Punctuation 68988
 
0.4%
Open Punctuation 68988
 
0.4%
Math Symbol 2214
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1214966
 
9.8%
R 1143991
 
9.3%
O 1105541
 
9.0%
I 1071746
 
8.7%
T 1049779
 
8.5%
N 755746
 
6.1%
U 708805
 
5.7%
V 681981
 
5.5%
S 655311
 
5.3%
A 546898
 
4.4%
Other values (15) 3404974
27.6%
Decimal Number
ValueCountFrequency (%)
1 592592
29.8%
0 440643
22.1%
9 216707
 
10.9%
5 173708
 
8.7%
4 131104
 
6.6%
8 125438
 
6.3%
6 92925
 
4.7%
2 87998
 
4.4%
3 70747
 
3.6%
7 59302
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 173886
88.0%
* 19542
 
9.9%
, 2861
 
1.4%
# 1405
 
0.7%
' 8
 
< 0.1%
. 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2961532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 980962
100.0%
Close Punctuation
ValueCountFrequency (%)
) 68988
100.0%
Open Punctuation
ValueCountFrequency (%)
( 68988
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12339738
66.3%
Common 6271555
33.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1214966
 
9.8%
R 1143991
 
9.3%
O 1105541
 
9.0%
I 1071746
 
8.7%
T 1049779
 
8.5%
N 755746
 
6.1%
U 708805
 
5.7%
V 681981
 
5.5%
S 655311
 
5.3%
A 546898
 
4.4%
Other values (15) 3404974
27.6%
Common
ValueCountFrequency (%)
2961532
47.2%
- 980962
 
15.6%
1 592592
 
9.4%
0 440643
 
7.0%
9 216707
 
3.5%
/ 173886
 
2.8%
5 173708
 
2.8%
4 131104
 
2.1%
8 125438
 
2.0%
6 92925
 
1.5%
Other values (11) 382058
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18611293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2961532
15.9%
E 1214966
 
6.5%
R 1143991
 
6.1%
O 1105541
 
5.9%
I 1071746
 
5.8%
T 1049779
 
5.6%
- 980962
 
5.3%
N 755746
 
4.1%
U 708805
 
3.8%
V 681981
 
3.7%
Other values (36) 6936244
37.3%

Type of Incident
Categorical

Distinct1103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
BMV
107570 
UNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILE
96716 
UNAUTHORIZED USE OF MOTOR VEH - TRUCK OR BUS
 
46336
FOUND PROPERTY (NO OFFENSE)
 
32506
CRIMINAL TRESPASS WARNING
 
29770
Other values (1098)
725605 

Length

Max length77
Median length69
Mean length34.0607
Min length3

Characters and Unicode

Total characters35372139
Distinct characters59
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186 ?
Unique (%)< 0.1%

Sample

1st rowTHEFT OF PROP (AUTO ACC) <$100 - (NOT EMP)
2nd rowHARASSMENT
3rd rowBURGLARY OF BUILDING - FORCED ENTRY
4th rowPUBLIC INTOXICATION
5th rowDEADLY CONDUCT DISCHARGE FIREARM

Common Values

ValueCountFrequency (%)
BMV 107570
 
10.4%
UNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILE 96716
 
9.3%
UNAUTHORIZED USE OF MOTOR VEH - TRUCK OR BUS 46336
 
4.5%
FOUND PROPERTY (NO OFFENSE) 32506
 
3.1%
CRIMINAL TRESPASS WARNING 29770
 
2.9%
BURGLARY OF HABITATION - FORCED ENTRY 28339
 
2.7%
PUBLIC INTOXICATION 27649
 
2.7%
BURGLARY OF BUILDING - FORCED ENTRY 27447
 
2.6%
CRIM MISCHIEF > OR EQUAL $100 < $750 27443
 
2.6%
ABANDONED PROPERTY (NO OFFENSE) 25671
 
2.5%
Other values (1093) 589056
56.7%

Length

2023-04-21T15:41:28.463868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
511101
 
8.5%
of 429132
 
7.2%
or 261635
 
4.4%
equal 198623
 
3.3%
veh 182564
 
3.0%
motor 164538
 
2.7%
unauthorized 163437
 
2.7%
use 163415
 
2.7%
no 160199
 
2.7%
offense 141386
 
2.4%
Other values (1136) 3621269
60.4%

Most occurring characters

ValueCountFrequency (%)
4962590
 
14.0%
O 2902664
 
8.2%
E 2530318
 
7.2%
R 2098607
 
5.9%
T 2018589
 
5.7%
I 1685056
 
4.8%
A 1681570
 
4.8%
N 1598093
 
4.5%
U 1506505
 
4.3%
F 1270788
 
3.6%
Other values (49) 13117359
37.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26307406
74.4%
Space Separator 4962590
 
14.0%
Decimal Number 1690407
 
4.8%
Math Symbol 449721
 
1.3%
Open Punctuation 428747
 
1.2%
Dash Punctuation 423193
 
1.2%
Close Punctuation 415821
 
1.2%
Currency Symbol 412876
 
1.2%
Other Punctuation 215966
 
0.6%
Lowercase Letter 65412
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2902664
 
11.0%
E 2530318
 
9.6%
R 2098607
 
8.0%
T 2018589
 
7.7%
I 1685056
 
6.4%
A 1681570
 
6.4%
N 1598093
 
6.1%
U 1506505
 
5.7%
F 1270788
 
4.8%
S 1162878
 
4.4%
Other values (16) 7852338
29.8%
Decimal Number
ValueCountFrequency (%)
0 742341
43.9%
5 264205
 
15.6%
1 204301
 
12.1%
3 166652
 
9.9%
2 155162
 
9.2%
7 121688
 
7.2%
4 29503
 
1.7%
8 5378
 
0.3%
6 1054
 
0.1%
9 123
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 55313
84.6%
b 7023
 
10.7%
c 1397
 
2.1%
f 724
 
1.1%
d 290
 
0.4%
o 227
 
0.3%
r 227
 
0.3%
g 211
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 82348
38.1%
, 76906
35.6%
/ 44907
20.8%
* 7375
 
3.4%
# 3265
 
1.5%
% 677
 
0.3%
: 488
 
0.2%
Math Symbol
ValueCountFrequency (%)
< 244123
54.3%
> 204612
45.5%
+ 986
 
0.2%
Space Separator
ValueCountFrequency (%)
4962590
100.0%
Open Punctuation
ValueCountFrequency (%)
( 428747
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 423193
100.0%
Close Punctuation
ValueCountFrequency (%)
) 415821
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 412876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26372818
74.6%
Common 8999321
 
25.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2902664
 
11.0%
E 2530318
 
9.6%
R 2098607
 
8.0%
T 2018589
 
7.7%
I 1685056
 
6.4%
A 1681570
 
6.4%
N 1598093
 
6.1%
U 1506505
 
5.7%
F 1270788
 
4.8%
S 1162878
 
4.4%
Other values (24) 7917750
30.0%
Common
ValueCountFrequency (%)
4962590
55.1%
0 742341
 
8.2%
( 428747
 
4.8%
- 423193
 
4.7%
) 415821
 
4.6%
$ 412876
 
4.6%
5 264205
 
2.9%
< 244123
 
2.7%
> 204612
 
2.3%
1 204301
 
2.3%
Other values (15) 696512
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35372139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4962590
 
14.0%
O 2902664
 
8.2%
E 2530318
 
7.2%
R 2098607
 
5.9%
T 2018589
 
5.7%
I 1685056
 
4.8%
A 1681570
 
4.8%
N 1598093
 
4.5%
U 1506505
 
4.3%
F 1270788
 
3.6%
Other values (49) 13117359
37.1%

Type Location
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct73
Distinct (%)< 0.1%
Missing1264
Missing (%)0.1%
Memory size7.9 MiB
Highway, Street, Alley ETC
187146 
Single Family Residence - Occupied
114523 
Apartment Parking Lot
100791 
Apartment Complex/Building
74493 
Parking (Business)
67962 
Other values (68)
492324 

Length

Max length40
Median length35
Mean length23.523146
Min length4

Characters and Unicode

Total characters24399124
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApartment Parking Lot
2nd rowOutdoor Area Public/Private
3rd rowRestaurant/Food Service/TABC Location
4th rowOutdoor Area Public/Private
5th rowSingle Family Residence - Occupied

Common Values

ValueCountFrequency (%)
Highway, Street, Alley ETC 187146
18.0%
Single Family Residence - Occupied 114523
11.0%
Apartment Parking Lot 100791
9.7%
Apartment Complex/Building 74493
 
7.2%
Parking (Business) 67962
 
6.5%
Outdoor Area Public/Private 64930
 
6.3%
Parking Lot (All Others) 61128
 
5.9%
Apartment Residence 55353
 
5.3%
Other 38608
 
3.7%
Retail Store 29482
 
2.8%
Other values (63) 242823
23.4%

Length

2023-04-21T15:41:28.674651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
parking 247933
 
7.8%
apartment 240115
 
7.6%
etc 196772
 
6.2%
highway 187146
 
5.9%
street 187146
 
5.9%
alley 187146
 
5.9%
residence 184793
 
5.8%
lot 175516
 
5.6%
133454
 
4.2%
family 127000
 
4.0%
Other values (112) 1292363
40.9%

Most occurring characters

ValueCountFrequency (%)
e 2510270
 
10.3%
2122145
 
8.7%
i 1696233
 
7.0%
t 1646573
 
6.7%
r 1280487
 
5.2%
a 1275227
 
5.2%
n 1249198
 
5.1%
l 1139807
 
4.7%
o 783525
 
3.2%
c 750530
 
3.1%
Other values (42) 9945129
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17348264
71.1%
Uppercase Letter 3819202
 
15.7%
Space Separator 2122145
 
8.7%
Other Punctuation 689105
 
2.8%
Open Punctuation 143477
 
0.6%
Close Punctuation 143477
 
0.6%
Dash Punctuation 133454
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2510270
14.5%
i 1696233
 
9.8%
t 1646573
 
9.5%
r 1280487
 
7.4%
a 1275227
 
7.4%
n 1249198
 
7.2%
l 1139807
 
6.6%
o 783525
 
4.5%
c 750530
 
4.3%
g 672640
 
3.9%
Other values (14) 4343774
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 587865
15.4%
S 465886
12.2%
P 412799
10.8%
C 382577
10.0%
O 321041
8.4%
T 249393
6.5%
R 241423
6.3%
E 213945
 
5.6%
H 212996
 
5.6%
L 204151
 
5.3%
Other values (11) 527126
13.8%
Other Punctuation
ValueCountFrequency (%)
, 374292
54.3%
/ 305187
44.3%
. 9626
 
1.4%
Space Separator
ValueCountFrequency (%)
2122145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143477
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143477
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 133454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21167466
86.8%
Common 3231658
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2510270
 
11.9%
i 1696233
 
8.0%
t 1646573
 
7.8%
r 1280487
 
6.0%
a 1275227
 
6.0%
n 1249198
 
5.9%
l 1139807
 
5.4%
o 783525
 
3.7%
c 750530
 
3.5%
g 672640
 
3.2%
Other values (35) 8162976
38.6%
Common
ValueCountFrequency (%)
2122145
65.7%
, 374292
 
11.6%
/ 305187
 
9.4%
( 143477
 
4.4%
) 143477
 
4.4%
- 133454
 
4.1%
. 9626
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24399124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2510270
 
10.3%
2122145
 
8.7%
i 1696233
 
7.0%
t 1646573
 
6.7%
r 1280487
 
5.2%
a 1275227
 
5.2%
n 1249198
 
5.1%
l 1139807
 
4.7%
o 783525
 
3.2%
c 750530
 
3.1%
Other values (42) 9945129
40.8%

Type of Property
Categorical

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)< 0.1%
Missing809505
Missing (%)77.9%
Memory size7.9 MiB
Motor Vehicle
61393 
Other
31620 
Apartment Complex/Building
25222 
Parking Lot
23844 
Residential Property Occupied/Vacant
23397 
Other values (35)
63522 

Length

Max length36
Median length27
Mean length17.471332
Min length3

Characters and Unicode

Total characters4000900
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowMotor Vehicle
2nd rowOther
3rd rowMotor Vehicle
4th rowResidential Property Occupied/Vacant
5th rowOutdoor Area Public/Private

Common Values

ValueCountFrequency (%)
Motor Vehicle 61393
 
5.9%
Other 31620
 
3.0%
Apartment Complex/Building 25222
 
2.4%
Parking Lot 23844
 
2.3%
Residential Property Occupied/Vacant 23397
 
2.3%
Outdoor Area Public/Private 15278
 
1.5%
None 15131
 
1.5%
Commercial Property Occupied/Vacant 10794
 
1.0%
Retail Store 8413
 
0.8%
Resturant/Food Service/Tabc Location 5347
 
0.5%
Other values (30) 8559
 
0.8%
(Missing) 809505
77.9%

Length

2023-04-21T15:41:28.753211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
motor 61393
13.2%
vehicle 61393
13.2%
property 34191
 
7.3%
occupied/vacant 34191
 
7.3%
other 31620
 
6.8%
apartment 25222
 
5.4%
complex/building 25222
 
5.4%
parking 23844
 
5.1%
lot 23844
 
5.1%
residential 23397
 
5.0%
Other values (46) 121196
26.0%

Most occurring characters

ValueCountFrequency (%)
e 423828
 
10.6%
t 331222
 
8.3%
o 296180
 
7.4%
i 296119
 
7.4%
r 288485
 
7.2%
236515
 
5.9%
c 213446
 
5.3%
a 210964
 
5.3%
l 173720
 
4.3%
n 169368
 
4.2%
Other values (41) 1361053
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3125904
78.1%
Uppercase Letter 552218
 
13.8%
Space Separator 236515
 
5.9%
Other Punctuation 85936
 
2.1%
Decimal Number 327
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 423828
13.6%
t 331222
10.6%
o 296180
9.5%
i 296119
9.5%
r 288485
9.2%
c 213446
 
6.8%
a 210964
 
6.7%
l 173720
 
5.6%
n 169368
 
5.4%
p 119377
 
3.8%
Other values (12) 603195
19.3%
Uppercase Letter
ValueCountFrequency (%)
V 96135
17.4%
P 88900
16.1%
O 85338
15.5%
M 62636
11.3%
A 40939
7.4%
R 38040
 
6.9%
C 36016
 
6.5%
B 29471
 
5.3%
L 29191
 
5.3%
N 15131
 
2.7%
Other values (7) 30421
 
5.5%
Decimal Number
ValueCountFrequency (%)
0 77
23.5%
1 59
18.0%
5 59
18.0%
3 49
15.0%
9 39
11.9%
7 18
 
5.5%
2 18
 
5.5%
6 4
 
1.2%
4 3
 
0.9%
8 1
 
0.3%
Space Separator
ValueCountFrequency (%)
236515
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 85936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3678122
91.9%
Common 322778
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 423828
 
11.5%
t 331222
 
9.0%
o 296180
 
8.1%
i 296119
 
8.1%
r 288485
 
7.8%
c 213446
 
5.8%
a 210964
 
5.7%
l 173720
 
4.7%
n 169368
 
4.6%
p 119377
 
3.2%
Other values (29) 1155413
31.4%
Common
ValueCountFrequency (%)
236515
73.3%
/ 85936
 
26.6%
0 77
 
< 0.1%
1 59
 
< 0.1%
5 59
 
< 0.1%
3 49
 
< 0.1%
9 39
 
< 0.1%
7 18
 
< 0.1%
2 18
 
< 0.1%
6 4
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 423828
 
10.6%
t 331222
 
8.3%
o 296180
 
7.4%
i 296119
 
7.4%
r 288485
 
7.2%
236515
 
5.9%
c 213446
 
5.3%
a 210964
 
5.3%
l 173720
 
4.3%
n 169368
 
4.2%
Other values (41) 1361053
34.0%

Incident Address
Categorical

Distinct195888
Distinct (%)18.9%
Missing3441
Missing (%)0.3%
Memory size7.9 MiB
1400 S LAMAR ST
 
3094
8687 N CENTRAL EXPY
 
2926
8008 HERB KELLEHER WAY
 
2416
1400 BOTHAM JEAN BLVD
 
1568
8687 N CENTRAL SERV SB
 
1559
Other values (195883)
1023499 

Length

Max length53
Median length39
Mean length17.226104
Min length1

Characters and Unicode

Total characters17830086
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94785 ?
Unique (%)9.2%

Sample

1st row7152 FAIR OAKS AVE
2nd row2525 PLEASANT DR
3rd row10443 N CENTRAL EXPY
4th row6336 ALCORN AVE
5th row226 LONGBRANCH LN

Common Values

ValueCountFrequency (%)
1400 S LAMAR ST 3094
 
0.3%
8687 N CENTRAL EXPY 2926
 
0.3%
8008 HERB KELLEHER WAY 2416
 
0.2%
1400 BOTHAM JEAN BLVD 1568
 
0.2%
8687 N CENTRAL SERV SB 1559
 
0.2%
9915 E NORTHWEST HWY 1206
 
0.1%
1600 FUN WAY 1189
 
0.1%
7401 SAMUELL BLVD 1188
 
0.1%
1521 N COCKRELL HILL RD 1186
 
0.1%
725 N JIM MILLER RD 1098
 
0.1%
Other values (195878) 1017632
98.0%
(Missing) 3441
 
0.3%

Length

2023-04-21T15:41:28.844849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rd 195653
 
5.3%
dr 175578
 
4.8%
st 171651
 
4.7%
ave 161108
 
4.4%
ln 97939
 
2.7%
n 82628
 
2.2%
blvd 81421
 
2.2%
s 77127
 
2.1%
w 60357
 
1.6%
e 42556
 
1.2%
Other values (22146) 2537967
68.9%

Most occurring characters

ValueCountFrequency (%)
2648923
 
14.9%
R 1093260
 
6.1%
E 1090097
 
6.1%
A 892425
 
5.0%
0 868598
 
4.9%
L 858041
 
4.8%
N 813641
 
4.6%
D 715302
 
4.0%
S 700588
 
3.9%
1 689276
 
3.9%
Other values (37) 7459935
41.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10959885
61.5%
Decimal Number 4220253
 
23.7%
Space Separator 2648923
 
14.9%
Other Punctuation 901
 
< 0.1%
Dash Punctuation 122
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1093260
 
10.0%
E 1090097
 
9.9%
A 892425
 
8.1%
L 858041
 
7.8%
N 813641
 
7.4%
D 715302
 
6.5%
S 700588
 
6.4%
T 669381
 
6.1%
O 589857
 
5.4%
I 473189
 
4.3%
Other values (16) 3064104
28.0%
Decimal Number
ValueCountFrequency (%)
0 868598
20.6%
1 689276
16.3%
2 499983
11.8%
3 436681
10.3%
5 384200
9.1%
4 322966
 
7.7%
7 263236
 
6.2%
9 253310
 
6.0%
8 251879
 
6.0%
6 250124
 
5.9%
Other Punctuation
ValueCountFrequency (%)
& 544
60.4%
. 298
33.1%
/ 38
 
4.2%
# 9
 
1.0%
, 9
 
1.0%
' 2
 
0.2%
; 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2648923
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 122
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10959885
61.5%
Common 6870201
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1093260
 
10.0%
E 1090097
 
9.9%
A 892425
 
8.1%
L 858041
 
7.8%
N 813641
 
7.4%
D 715302
 
6.5%
S 700588
 
6.4%
T 669381
 
6.1%
O 589857
 
5.4%
I 473189
 
4.3%
Other values (16) 3064104
28.0%
Common
ValueCountFrequency (%)
2648923
38.6%
0 868598
 
12.6%
1 689276
 
10.0%
2 499983
 
7.3%
3 436681
 
6.4%
5 384200
 
5.6%
4 322966
 
4.7%
7 263236
 
3.8%
9 253310
 
3.7%
8 251879
 
3.7%
Other values (11) 251149
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17830086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2648923
 
14.9%
R 1093260
 
6.1%
E 1090097
 
6.1%
A 892425
 
5.0%
0 868598
 
4.9%
L 858041
 
4.8%
N 813641
 
4.6%
D 715302
 
4.0%
S 700588
 
3.9%
1 689276
 
3.9%
Other values (37) 7459935
41.8%

Apartment Number
Categorical

HIGH CARDINALITY  MISSING 

Distinct13956
Distinct (%)6.0%
Missing804068
Missing (%)77.4%
Memory size7.9 MiB
100
 
2624
101
 
2121
A
 
2037
102
 
1967
103
 
1756
Other values (13951)
223930 

Length

Max length10
Median length9
Mean length3.4692644
Min length1

Characters and Unicode

Total characters813317
Distinct characters76
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6565 ?
Unique (%)2.8%

Sample

1st row1015
2nd row254
3rd row114
4th row327
5th row137

Common Values

ValueCountFrequency (%)
100 2624
 
0.3%
101 2121
 
0.2%
A 2037
 
0.2%
102 1967
 
0.2%
103 1756
 
0.2%
104 1610
 
0.2%
B 1609
 
0.2%
110 1507
 
0.1%
OFFICE 1259
 
0.1%
105 1252
 
0.1%
Other values (13946) 216693
 
20.9%
(Missing) 804068
77.4%

Length

2023-04-21T15:41:28.917513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 2737
 
1.1%
a 2181
 
0.9%
101 2151
 
0.9%
102 1995
 
0.8%
103 1784
 
0.7%
b 1746
 
0.7%
104 1626
 
0.7%
110 1540
 
0.6%
105 1290
 
0.5%
ofc 1285
 
0.5%
Other values (11965) 221789
92.4%

Most occurring characters

ValueCountFrequency (%)
1 187264
23.0%
0 128582
15.8%
2 124957
15.4%
3 72583
 
8.9%
4 54308
 
6.7%
5 43884
 
5.4%
6 36682
 
4.5%
7 32715
 
4.0%
8 30249
 
3.7%
9 24955
 
3.1%
Other values (66) 77138
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 736179
90.5%
Uppercase Letter 68218
 
8.4%
Space Separator 5713
 
0.7%
Dash Punctuation 1567
 
0.2%
Other Punctuation 1188
 
0.1%
Math Symbol 161
 
< 0.1%
Lowercase Letter 161
 
< 0.1%
Modifier Symbol 80
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Connector Punctuation 19
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7408
10.9%
F 6141
 
9.0%
E 6062
 
8.9%
C 5950
 
8.7%
B 5801
 
8.5%
O 5363
 
7.9%
D 3895
 
5.7%
T 3547
 
5.2%
S 3153
 
4.6%
I 3094
 
4.5%
Other values (16) 17804
26.1%
Lowercase Letter
ValueCountFrequency (%)
n 46
28.6%
a 41
25.5%
c 12
 
7.5%
u 11
 
6.8%
e 9
 
5.6%
o 7
 
4.3%
r 7
 
4.3%
p 5
 
3.1%
l 4
 
2.5%
b 4
 
2.5%
Other values (7) 15
 
9.3%
Other Punctuation
ValueCountFrequency (%)
# 756
63.6%
. 160
 
13.5%
/ 104
 
8.8%
, 64
 
5.4%
& 29
 
2.4%
: 28
 
2.4%
* 19
 
1.6%
? 9
 
0.8%
' 8
 
0.7%
\ 5
 
0.4%
Other values (2) 6
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 187264
25.4%
0 128582
17.5%
2 124957
17.0%
3 72583
 
9.9%
4 54308
 
7.4%
5 43884
 
6.0%
6 36682
 
5.0%
7 32715
 
4.4%
8 30249
 
4.1%
9 24955
 
3.4%
Math Symbol
ValueCountFrequency (%)
= 154
95.7%
+ 6
 
3.7%
~ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
5713
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1567
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Control
ValueCountFrequency (%)
 2
100.0%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 744938
91.6%
Latin 68379
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7408
10.8%
F 6141
 
9.0%
E 6062
 
8.9%
C 5950
 
8.7%
B 5801
 
8.5%
O 5363
 
7.8%
D 3895
 
5.7%
T 3547
 
5.2%
S 3153
 
4.6%
I 3094
 
4.5%
Other values (33) 17965
26.3%
Common
ValueCountFrequency (%)
1 187264
25.1%
0 128582
17.3%
2 124957
16.8%
3 72583
 
9.7%
4 54308
 
7.3%
5 43884
 
5.9%
6 36682
 
4.9%
7 32715
 
4.4%
8 30249
 
4.1%
9 24955
 
3.3%
Other values (23) 8759
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 813315
> 99.9%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 187264
23.0%
0 128582
15.8%
2 124957
15.4%
3 72583
 
8.9%
4 54308
 
6.7%
5 43884
 
5.4%
6 36682
 
4.5%
7 32715
 
4.0%
8 30249
 
3.7%
9 24955
 
3.1%
Other values (65) 77136
9.5%
Specials
ValueCountFrequency (%)
� 2
100.0%

Reporting Area
Real number (ℝ)

Distinct1151
Distinct (%)0.1%
Missing745
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3144.6108
Minimum1001
Maximum9611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:28.990836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1030
Q11248
median3059
Q34321
95-th percentile6040
Maximum9611
Range8610
Interquartile range (IQR)3073

Descriptive statistics

Standard deviation1827.3148
Coefficient of variation (CV)0.5810941
Kurtosis1.7942587
Mean3144.6108
Median Absolute Deviation (MAD)1288
Skewness1.0764843
Sum3.263345 × 109
Variance3339079.3
MonotonicityNot monotonic
2023-04-21T15:41:29.066379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1217 9681
 
0.9%
4360 6825
 
0.7%
1030 6662
 
0.6%
1042 5878
 
0.6%
1008 5743
 
0.6%
3059 5552
 
0.5%
4403 5544
 
0.5%
2123 5532
 
0.5%
1084 5425
 
0.5%
1027 5167
 
0.5%
Other values (1141) 975749
94.0%
ValueCountFrequency (%)
1001 1097
 
0.1%
1002 230
 
< 0.1%
1003 864
 
0.1%
1004 2472
0.2%
1005 399
 
< 0.1%
1006 1468
 
0.1%
1007 699
 
0.1%
1008 5743
0.6%
1009 4169
0.4%
1010 1018
 
0.1%
ValueCountFrequency (%)
9611 1353
 
0.1%
9610 3961
0.4%
9609 758
 
0.1%
9608 1236
 
0.1%
9607 1845
0.2%
9606 478
 
< 0.1%
9605 1570
 
0.2%
9604 3113
0.3%
9603 1109
 
0.1%
9602 1253
 
0.1%

Beat
Real number (ℝ)

Distinct235
Distinct (%)< 0.1%
Missing482
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean414.16255
Minimum7
Maximum757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:29.139930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile131
Q1236
median421
Q3552
95-th percentile742
Maximum757
Range750
Interquartile range (IQR)316

Descriptive statistics

Standard deviation197.14755
Coefficient of variation (CV)0.47601491
Kurtosis-1.1641548
Mean414.16255
Median Absolute Deviation (MAD)179
Skewness0.14281427
Sum4.2990943 × 108
Variance38867.156
MonotonicityNot monotonic
2023-04-21T15:41:29.216026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521 11910
 
1.1%
153 11862
 
1.1%
534 10109
 
1.0%
523 9747
 
0.9%
318 9681
 
0.9%
154 9309
 
0.9%
132 8890
 
0.9%
143 8826
 
0.8%
614 8583
 
0.8%
131 8548
 
0.8%
Other values (225) 940556
90.6%
ValueCountFrequency (%)
7 1
 
< 0.1%
111 3560
0.3%
112 3675
0.4%
113 3676
0.4%
114 4890
0.5%
115 5982
0.6%
116 2461
 
0.2%
121 5591
0.5%
122 6210
0.6%
123 2999
0.3%
ValueCountFrequency (%)
757 2014
0.2%
756 4988
0.5%
755 2985
0.3%
754 2754
0.3%
753 2840
0.3%
752 2718
0.3%
751 3908
0.4%
748 3543
0.3%
747 3420
0.3%
746 3076
0.3%

Division
Categorical

Distinct14
Distinct (%)< 0.1%
Missing482
Missing (%)< 0.1%
Memory size7.9 MiB
NORTHEAST
170906 
CENTRAL
162440 
NORTHWEST
158930 
SOUTHWEST
153704 
SOUTHEAST
148526 
Other values (9)
243515 

Length

Max length13
Median length9
Mean length9.6223959
Min length7

Characters and Unicode

Total characters9988249
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTHEAST
2nd rowSOUTHEAST
3rd rowNORTH CENTRAL
4th rowSOUTHEAST
5th rowSOUTHEAST

Common Values

ValueCountFrequency (%)
NORTHEAST 170906
16.5%
CENTRAL 162440
15.6%
NORTHWEST 158930
15.3%
SOUTHWEST 153704
14.8%
SOUTHEAST 148526
14.3%
SOUTH CENTRAL 130339
12.6%
NORTH CENTRAL 112299
10.8%
NorthEast 184
 
< 0.1%
Central 178
 
< 0.1%
SouthWest 120
 
< 0.1%
Other values (4) 395
 
< 0.1%
(Missing) 482
 
< 0.1%

Length

2023-04-21T15:41:29.290491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central 405442
31.7%
northeast 171090
13.4%
northwest 159035
 
12.4%
southwest 153824
 
12.0%
southeast 148630
 
11.6%
south 130443
 
10.2%
north 112381
 
8.8%

Most occurring characters

ValueCountFrequency (%)
T 1911848
19.1%
S 1064963
10.7%
E 1037432
10.4%
O 874704
8.8%
H 874704
8.8%
N 847584
8.5%
R 847213
8.5%
A 724510
 
7.3%
U 432569
 
4.3%
C 405442
 
4.1%
Other values (13) 967280
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9738906
97.5%
Space Separator 242824
 
2.4%
Lowercase Letter 6519
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1911848
19.6%
S 1064963
10.9%
E 1037432
10.7%
O 874704
9.0%
H 874704
9.0%
N 847584
8.7%
R 847213
8.7%
A 724510
 
7.4%
U 432569
 
4.4%
C 405442
 
4.2%
Other values (2) 717937
 
7.4%
Lowercase Letter
ValueCountFrequency (%)
t 1576
24.2%
r 735
11.3%
o 699
10.7%
h 699
10.7%
a 652
10.0%
e 589
 
9.0%
s 513
 
7.9%
n 364
 
5.6%
l 364
 
5.6%
u 328
 
5.0%
Space Separator
ValueCountFrequency (%)
242824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9745425
97.6%
Common 242824
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1911848
19.6%
S 1064963
10.9%
E 1037432
10.6%
O 874704
9.0%
H 874704
9.0%
N 847584
8.7%
R 847213
8.7%
A 724510
 
7.4%
U 432569
 
4.4%
C 405442
 
4.2%
Other values (12) 724456
 
7.4%
Common
ValueCountFrequency (%)
242824
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9988249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1911848
19.1%
S 1064963
10.7%
E 1037432
10.4%
O 874704
8.8%
H 874704
8.8%
N 847584
8.5%
R 847213
8.5%
A 724510
 
7.3%
U 432569
 
4.3%
C 405442
 
4.1%
Other values (13) 967280
9.7%

Sector
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing282
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean410.16425
Minimum0
Maximum750
Zeros200
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:29.357711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile130
Q1230
median420
Q3550
95-th percentile740
Maximum750
Range750
Interquartile range (IQR)320

Descriptive statistics

Standard deviation197.28755
Coefficient of variation (CV)0.48099645
Kurtosis-1.170254
Mean410.16425
Median Absolute Deviation (MAD)180
Skewness0.14407978
Sum4.2584114 × 108
Variance38922.376
MonotonicityNot monotonic
2023-04-21T15:41:29.424009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
150 41188
 
4.0%
530 40115
 
3.9%
130 39979
 
3.8%
520 38821
 
3.7%
250 37724
 
3.6%
210 36924
 
3.6%
230 35616
 
3.4%
410 35300
 
3.4%
740 34492
 
3.3%
220 34209
 
3.3%
Other values (27) 663853
63.9%
ValueCountFrequency (%)
0 200
 
< 0.1%
70 1
 
< 0.1%
110 24244
2.3%
120 27405
2.6%
130 39979
3.8%
140 29802
2.9%
150 41188
4.0%
210 36924
3.6%
220 34209
3.3%
230 35616
3.4%
ValueCountFrequency (%)
750 22207
2.1%
740 34492
3.3%
730 25927
2.5%
720 27153
2.6%
710 20663
2.0%
650 19639
1.9%
640 21531
2.1%
630 25439
2.4%
620 22292
2.1%
610 23480
2.3%

Council District
Categorical

Distinct19
Distinct (%)< 0.1%
Missing1250
Missing (%)0.1%
Memory size7.9 MiB
D2
125244 
D6
113658 
D14
103374 
D7
97916 
D8
86236 
Other values (14)
510825 

Length

Max length3
Median length2
Mean length2.3106889
Min length1

Characters and Unicode

Total characters2396769
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD13
2nd rowD5
3rd rowD11
4th rowD8
5th rowD8

Common Values

ValueCountFrequency (%)
D2 125244
12.1%
D6 113658
10.9%
D14 103374
10.0%
D7 97916
9.4%
D8 86236
8.3%
D4 75567
 
7.3%
D11 63169
 
6.1%
D10 61737
 
5.9%
D3 60832
 
5.9%
D13 57516
 
5.5%
Other values (9) 192004
18.5%

Length

2023-04-21T15:41:29.500103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d2 125244
12.1%
d6 113658
11.0%
d14 103374
10.0%
d7 97916
9.4%
d8 86236
8.3%
d4 75567
 
7.3%
d11 63169
 
6.1%
d10 61737
 
6.0%
d3 60832
 
5.9%
d13 57516
 
5.5%
Other values (9) 192004
18.5%

Most occurring characters

ValueCountFrequency (%)
D 1037142
43.3%
1 438314
18.3%
4 178941
 
7.5%
2 161786
 
6.8%
3 118348
 
4.9%
6 113658
 
4.7%
7 97916
 
4.1%
8 86252
 
3.6%
0 61757
 
2.6%
9 51336
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1359627
56.7%
Uppercase Letter 1037142
43.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 438314
32.2%
4 178941
13.2%
2 161786
 
11.9%
3 118348
 
8.7%
6 113658
 
8.4%
7 97916
 
7.2%
8 86252
 
6.3%
0 61757
 
4.5%
9 51336
 
3.8%
5 51319
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
D 1037142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1359627
56.7%
Latin 1037142
43.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 438314
32.2%
4 178941
13.2%
2 161786
 
11.9%
3 118348
 
8.7%
6 113658
 
8.4%
7 97916
 
7.2%
8 86252
 
6.3%
0 61757
 
4.5%
9 51336
 
3.8%
5 51319
 
3.8%
Latin
ValueCountFrequency (%)
D 1037142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2396769
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1037142
43.3%
1 438314
18.3%
4 178941
 
7.5%
2 161786
 
6.8%
3 118348
 
4.9%
6 113658
 
4.7%
7 97916
 
4.1%
8 86252
 
3.6%
0 61757
 
2.6%
9 51336
 
2.1%

Target Area Action Grids
Categorical

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)< 0.1%
Missing661759
Missing (%)63.7%
Memory size7.9 MiB
WebbChapel Timberline
 
22372
Ross Bennett
 
22172
Monument GoodLatimer
 
21951
Five Points
 
19683
Forest Audelia
 
16945
Other values (26)
273621 

Length

Max length25
Median length20
Mean length16.615038
Min length10

Characters and Unicode

Total characters6259616
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFive Points
2nd rowLoop12 JimMiller
3rd rowLoop12 JimMiller
4th rowMonument GoodLatimer
5th rowLakeJune Buckner

Common Values

ValueCountFrequency (%)
WebbChapel Timberline 22372
 
2.2%
Ross Bennett 22172
 
2.1%
Monument GoodLatimer 21951
 
2.1%
Five Points 19683
 
1.9%
Forest Audelia 16945
 
1.6%
SpringValley Preston 15707
 
1.5%
Wycliff Lemmon 14675
 
1.4%
McKinney Allen 14152
 
1.4%
Jefferson Corridor 14124
 
1.4%
NWHwy WaltonWalker 13152
 
1.3%
Other values (21) 201811
 
19.4%
(Missing) 661759
63.7%

Length

2023-04-21T15:41:29.572779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buckner 33798
 
4.4%
forest 29152
 
3.8%
springvalley 26376
 
3.4%
campwisdom 23635
 
3.1%
webbchapel 22372
 
2.9%
timberline 22372
 
2.9%
ross 22172
 
2.9%
bennett 22172
 
2.9%
monument 21951
 
2.8%
goodlatimer 21951
 
2.8%
Other values (46) 524600
68.1%

Most occurring characters

ValueCountFrequency (%)
e 726782
 
11.6%
n 513020
 
8.2%
393807
 
6.3%
t 381778
 
6.1%
o 381174
 
6.1%
r 374274
 
6.0%
i 367139
 
5.9%
l 327376
 
5.2%
s 268168
 
4.3%
a 249944
 
4.0%
Other values (37) 2276154
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4833462
77.2%
Uppercase Letter 990131
 
15.8%
Space Separator 393807
 
6.3%
Decimal Number 42216
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 726782
15.0%
n 513020
10.6%
t 381778
 
7.9%
o 381174
 
7.9%
r 374274
 
7.7%
i 367139
 
7.6%
l 327376
 
6.8%
s 268168
 
5.5%
a 249944
 
5.2%
u 197147
 
4.1%
Other values (12) 1046660
21.7%
Uppercase Letter
ValueCountFrequency (%)
W 142834
14.4%
C 111455
11.3%
S 99290
10.0%
J 87127
8.8%
B 86464
8.7%
L 77097
 
7.8%
F 56606
 
5.7%
M 45313
 
4.6%
A 42587
 
4.3%
P 41599
 
4.2%
Other values (10) 199759
20.2%
Decimal Number
ValueCountFrequency (%)
3 11898
28.2%
0 11898
28.2%
1 9210
21.8%
2 9210
21.8%
Space Separator
ValueCountFrequency (%)
393807
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5823593
93.0%
Common 436023
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 726782
 
12.5%
n 513020
 
8.8%
t 381778
 
6.6%
o 381174
 
6.5%
r 374274
 
6.4%
i 367139
 
6.3%
l 327376
 
5.6%
s 268168
 
4.6%
a 249944
 
4.3%
u 197147
 
3.4%
Other values (32) 2036791
35.0%
Common
ValueCountFrequency (%)
393807
90.3%
3 11898
 
2.7%
0 11898
 
2.7%
1 9210
 
2.1%
2 9210
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6259616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 726782
 
11.6%
n 513020
 
8.2%
393807
 
6.3%
t 381778
 
6.1%
o 381174
 
6.1%
r 374274
 
6.0%
i 367139
 
5.9%
l 327376
 
5.2%
s 268168
 
4.3%
a 249944
 
4.0%
Other values (37) 2276154
36.4%

Community
Categorical

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)< 0.1%
Missing924457
Missing (%)89.0%
Memory size7.9 MiB
Northwest_PFA
16874 
ForestAudelia_PFA
12775 
Chaucer_PFA
9841 
KitMaham_PFA
8903 
FergusonWoodmeadow_PFA
 
5676
Other values (22)
59977 

Length

Max length22
Median length17
Mean length14.441988
Min length7

Characters and Unicode

Total characters1647051
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVickery Meadows_PFA
2nd rowFergusonWoodmeadow_PFA
3rd rowMalcolm_PFA
4th rowKitMaham_PFA
5th rowChaucer_PFA

Common Values

ValueCountFrequency (%)
Northwest_PFA 16874
 
1.6%
ForestAudelia_PFA 12775
 
1.2%
Chaucer_PFA 9841
 
0.9%
KitMaham_PFA 8903
 
0.9%
FergusonWoodmeadow_PFA 5676
 
0.5%
Bachman Lake_PFA 5484
 
0.5%
Malcolm_PFA 5153
 
0.5%
BryanHenderson_PFA 4728
 
0.5%
KiestPolk_PFA 4302
 
0.4%
MLK_PFA 4221
 
0.4%
Other values (17) 36089
 
3.5%
(Missing) 924457
89.0%

Length

2023-04-21T15:41:29.643188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
northwest_pfa 16874
 
12.4%
forestaudelia_pfa 12775
 
9.4%
chaucer_pfa 9841
 
7.3%
kitmaham_pfa 8903
 
6.6%
fergusonwoodmeadow_pfa 5676
 
4.2%
bachman 5484
 
4.0%
lake_pfa 5484
 
4.0%
malcolm_pfa 5153
 
3.8%
bryanhenderson_pfa 4728
 
3.5%
kiestpolk_pfa 4302
 
3.2%
Other values (27) 56500
41.6%

Most occurring characters

ValueCountFrequency (%)
F 135898
 
8.3%
P 126318
 
7.7%
A 125604
 
7.6%
e 115558
 
7.0%
_ 111255
 
6.8%
a 108498
 
6.6%
o 96511
 
5.9%
t 89766
 
5.5%
r 80394
 
4.9%
s 63655
 
3.9%
Other values (34) 593594
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 977739
59.4%
Uppercase Letter 536383
32.6%
Connector Punctuation 111255
 
6.8%
Space Separator 21674
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 115558
11.8%
a 108498
11.1%
o 96511
9.9%
t 89766
9.2%
r 80394
 
8.2%
s 63655
 
6.5%
i 56980
 
5.8%
h 52482
 
5.4%
l 51404
 
5.3%
u 43267
 
4.4%
Other values (12) 219224
22.4%
Uppercase Letter
ValueCountFrequency (%)
F 135898
25.3%
P 126318
23.5%
A 125604
23.4%
M 26991
 
5.0%
C 23111
 
4.3%
K 17426
 
3.2%
N 16874
 
3.1%
B 13095
 
2.4%
L 11695
 
2.2%
W 9060
 
1.7%
Other values (10) 30311
 
5.7%
Connector Punctuation
ValueCountFrequency (%)
_ 111255
100.0%
Space Separator
ValueCountFrequency (%)
21674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1514122
91.9%
Common 132929
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 135898
 
9.0%
P 126318
 
8.3%
A 125604
 
8.3%
e 115558
 
7.6%
a 108498
 
7.2%
o 96511
 
6.4%
t 89766
 
5.9%
r 80394
 
5.3%
s 63655
 
4.2%
i 56980
 
3.8%
Other values (32) 514940
34.0%
Common
ValueCountFrequency (%)
_ 111255
83.7%
21674
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1647051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 135898
 
8.3%
P 126318
 
7.7%
A 125604
 
7.6%
e 115558
 
7.0%
_ 111255
 
6.8%
a 108498
 
6.6%
o 96511
 
5.9%
t 89766
 
5.5%
r 80394
 
4.9%
s 63655
 
3.9%
Other values (34) 593594
36.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
00:00.0
1038503 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7269521
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 1038503
100.0%

Length

2023-04-21T15:41:29.710120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:29.771083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 1038503
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5192515
71.4%
: 1038503
 
14.3%
. 1038503
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5192515
71.4%
Other Punctuation 2077006
 
28.6%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
: 1038503
50.0%
. 1038503
50.0%
Decimal Number
ValueCountFrequency (%)
0 5192515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7269521
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5192515
71.4%
: 1038503
 
14.3%
. 1038503
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7269521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5192515
71.4%
: 1038503
 
14.3%
. 1038503
 
14.3%

Year1 of Occurrence
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5722
Minimum1974
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:29.825689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile2014
Q12017
median2019
Q32021
95-th percentile2022
Maximum2023
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.491761
Coefficient of variation (CV)0.0012344176
Kurtosis-0.42584613
Mean2018.5722
Median Absolute Deviation (MAD)2
Skewness-0.27229992
Sum2.0962933 × 109
Variance6.2088729
MonotonicityNot monotonic
2023-04-21T15:41:29.895562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2021 138987
13.4%
2022 136775
13.2%
2020 134614
13.0%
2019 134528
13.0%
2018 124522
12.0%
2016 103002
9.9%
2017 98460
9.5%
2015 96268
9.3%
2014 58789
5.7%
2023 11963
 
1.2%
Other values (28) 595
 
0.1%
ValueCountFrequency (%)
1974 1
 
< 0.1%
1975 1
 
< 0.1%
1980 1
 
< 0.1%
1982 1
 
< 0.1%
1983 1
 
< 0.1%
1989 3
< 0.1%
1990 1
 
< 0.1%
1992 1
 
< 0.1%
1993 1
 
< 0.1%
1995 1
 
< 0.1%
ValueCountFrequency (%)
2023 11963
 
1.2%
2022 136775
13.2%
2021 138987
13.4%
2020 134614
13.0%
2019 134528
13.0%
2018 124522
12.0%
2017 98460
9.5%
2016 103002
9.9%
2015 96268
9.3%
2014 58789
5.7%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
July
97070 
August
95715 
October
92850 
September
90891 
December
90156 
Other values (7)
571821 

Length

Max length9
Median length7
Mean length6.1651493
Min length3

Characters and Unicode

Total characters6402526
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowDecember
3rd rowDecember
4th rowDecember
5th rowDecember

Common Values

ValueCountFrequency (%)
July 97070
9.3%
August 95715
9.2%
October 92850
8.9%
September 90891
8.8%
December 90156
8.7%
June 89512
8.6%
January 89063
8.6%
November 86483
8.3%
May 82790
8.0%
March 77545
7.5%
Other values (2) 146428
14.1%

Length

2023-04-21T15:41:29.964477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
july 97070
9.3%
august 95715
9.2%
october 92850
8.9%
september 90891
8.8%
december 90156
8.7%
june 89512
8.6%
january 89063
8.6%
november 86483
8.3%
may 82790
8.0%
march 77545
7.5%
Other values (2) 146428
14.1%

Most occurring characters

ValueCountFrequency (%)
e 968186
15.1%
r 743133
 
11.6%
u 536792
 
8.4%
b 430097
 
6.7%
a 408178
 
6.4%
y 338640
 
5.3%
t 279456
 
4.4%
J 275645
 
4.3%
m 267530
 
4.2%
c 260551
 
4.1%
Other values (16) 1894318
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5364023
83.8%
Uppercase Letter 1038503
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 968186
18.0%
r 743133
13.9%
u 536792
10.0%
b 430097
8.0%
a 408178
7.6%
y 338640
 
6.3%
t 279456
 
5.2%
m 267530
 
5.0%
c 260551
 
4.9%
o 179333
 
3.3%
Other values (8) 952127
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 275645
26.5%
A 172426
16.6%
M 160335
15.4%
O 92850
 
8.9%
S 90891
 
8.8%
D 90156
 
8.7%
N 86483
 
8.3%
F 69717
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6402526
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 968186
15.1%
r 743133
 
11.6%
u 536792
 
8.4%
b 430097
 
6.7%
a 408178
 
6.4%
y 338640
 
5.3%
t 279456
 
4.4%
J 275645
 
4.3%
m 267530
 
4.2%
c 260551
 
4.1%
Other values (16) 1894318
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6402526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 968186
15.1%
r 743133
 
11.6%
u 536792
 
8.4%
b 430097
 
6.7%
a 408178
 
6.4%
y 338640
 
5.3%
t 279456
 
4.4%
J 275645
 
4.3%
m 267530
 
4.2%
c 260551
 
4.1%
Other values (16) 1894318
29.6%

Day1 of the Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Fri
158210 
Sat
154160 
Mon
147672 
Sun
147222 
Thu
145252 
Other values (2)
285987 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3115509
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTue
2nd rowFri
3rd rowSun
4th rowMon
5th rowWed

Common Values

ValueCountFrequency (%)
Fri 158210
15.2%
Sat 154160
14.8%
Mon 147672
14.2%
Sun 147222
14.2%
Thu 145252
14.0%
Wed 143279
13.8%
Tue 142708
13.7%

Length

2023-04-21T15:41:30.024659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:30.095741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
fri 158210
15.2%
sat 154160
14.8%
mon 147672
14.2%
sun 147222
14.2%
thu 145252
14.0%
wed 143279
13.8%
tue 142708
13.7%

Most occurring characters

ValueCountFrequency (%)
u 435182
14.0%
S 301382
9.7%
n 294894
9.5%
T 287960
 
9.2%
e 285987
 
9.2%
F 158210
 
5.1%
r 158210
 
5.1%
i 158210
 
5.1%
a 154160
 
4.9%
t 154160
 
4.9%
Other values (5) 727154
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2077006
66.7%
Uppercase Letter 1038503
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 435182
21.0%
n 294894
14.2%
e 285987
13.8%
r 158210
 
7.6%
i 158210
 
7.6%
a 154160
 
7.4%
t 154160
 
7.4%
o 147672
 
7.1%
h 145252
 
7.0%
d 143279
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
S 301382
29.0%
T 287960
27.7%
F 158210
15.2%
M 147672
14.2%
W 143279
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3115509
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 435182
14.0%
S 301382
9.7%
n 294894
9.5%
T 287960
 
9.2%
e 285987
 
9.2%
F 158210
 
5.1%
r 158210
 
5.1%
i 158210
 
5.1%
a 154160
 
4.9%
t 154160
 
4.9%
Other values (5) 727154
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3115509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 435182
14.0%
S 301382
9.7%
n 294894
9.5%
T 287960
 
9.2%
e 285987
 
9.2%
F 158210
 
5.1%
r 158210
 
5.1%
i 158210
 
5.1%
a 154160
 
4.9%
t 154160
 
4.9%
Other values (5) 727154
23.3%
Distinct1440
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
0:00
 
39168
22:00
 
29342
18:00
 
27866
20:00
 
26654
12:00
 
25989
Other values (1435)
889484 

Length

Max length5
Median length5
Mean length4.6698363
Min length4

Characters and Unicode

Total characters4849639
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20:00
2nd row15:16
3rd row0:00
4th row2:30
5th row22:21

Common Values

ValueCountFrequency (%)
0:00 39168
 
3.8%
22:00 29342
 
2.8%
18:00 27866
 
2.7%
20:00 26654
 
2.6%
12:00 25989
 
2.5%
17:00 25494
 
2.5%
21:00 24232
 
2.3%
19:00 24141
 
2.3%
23:00 20779
 
2.0%
8:00 20733
 
2.0%
Other values (1430) 774105
74.5%

Length

2023-04-21T15:41:30.165907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0:00 39168
 
3.8%
22:00 29342
 
2.8%
18:00 27866
 
2.7%
20:00 26654
 
2.6%
12:00 25989
 
2.5%
17:00 25494
 
2.5%
21:00 24232
 
2.3%
19:00 24141
 
2.3%
23:00 20779
 
2.0%
8:00 20733
 
2.0%
Other values (1430) 774105
74.5%

Most occurring characters

ValueCountFrequency (%)
0 1384502
28.5%
: 1038503
21.4%
1 723126
14.9%
2 466598
 
9.6%
3 336141
 
6.9%
5 275508
 
5.7%
4 188217
 
3.9%
8 124580
 
2.6%
9 113052
 
2.3%
7 106885
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3811136
78.6%
Other Punctuation 1038503
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1384502
36.3%
1 723126
19.0%
2 466598
 
12.2%
3 336141
 
8.8%
5 275508
 
7.2%
4 188217
 
4.9%
8 124580
 
3.3%
9 113052
 
3.0%
7 106885
 
2.8%
6 92527
 
2.4%
Other Punctuation
ValueCountFrequency (%)
: 1038503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4849639
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1384502
28.5%
: 1038503
21.4%
1 723126
14.9%
2 466598
 
9.6%
3 336141
 
6.9%
5 275508
 
5.7%
4 188217
 
3.9%
8 124580
 
2.6%
9 113052
 
2.3%
7 106885
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4849639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1384502
28.5%
: 1038503
21.4%
1 723126
14.9%
2 466598
 
9.6%
3 336141
 
6.9%
5 275508
 
5.7%
4 188217
 
3.9%
8 124580
 
2.6%
9 113052
 
2.3%
7 106885
 
2.2%

Day1 of the Year
Real number (ℝ)

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.94538
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:30.238242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1100
median192
Q3277
95-th percentile348
Maximum366
Range365
Interquartile range (IQR)177

Descriptive statistics

Standard deviation104.49892
Coefficient of variation (CV)0.55600687
Kurtosis-1.146155
Mean187.94538
Median Absolute Deviation (MAD)88
Skewness-0.091288177
Sum1.9518184 × 108
Variance10920.025
MonotonicityNot monotonic
2023-04-21T15:41:30.313455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4065
 
0.4%
182 3496
 
0.3%
274 3484
 
0.3%
152 3462
 
0.3%
244 3398
 
0.3%
183 3357
 
0.3%
213 3345
 
0.3%
226 3271
 
0.3%
200 3271
 
0.3%
335 3265
 
0.3%
Other values (356) 1004089
96.7%
ValueCountFrequency (%)
1 4065
0.4%
2 2721
0.3%
3 2773
0.3%
4 2883
0.3%
5 2934
0.3%
6 2941
0.3%
7 2932
0.3%
8 2801
0.3%
9 2696
0.3%
10 2805
0.3%
ValueCountFrequency (%)
366 700
 
0.1%
365 3056
0.3%
364 2896
0.3%
363 2869
0.3%
362 2722
0.3%
361 2900
0.3%
360 2501
0.2%
359 2330
0.2%
358 2785
0.3%
357 3001
0.3%
Distinct1
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Memory size7.9 MiB
00:00.0
1038488 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7269416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 1038488
> 99.9%
(Missing) 15
 
< 0.1%

Length

2023-04-21T15:41:30.383338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:30.439818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 1038488
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5192440
71.4%
: 1038488
 
14.3%
. 1038488
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5192440
71.4%
Other Punctuation 2076976
 
28.6%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
: 1038488
50.0%
. 1038488
50.0%
Decimal Number
ValueCountFrequency (%)
0 5192440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7269416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5192440
71.4%
: 1038488
 
14.3%
. 1038488
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5192440
71.4%
: 1038488
 
14.3%
. 1038488
 
14.3%

Year2 of Occurrence
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2018.5831
Minimum1982
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:30.491140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile2014
Q12017
median2019
Q32021
95-th percentile2022
Maximum2023
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4838366
Coefficient of variation (CV)0.0012304851
Kurtosis-0.90714795
Mean2018.5831
Median Absolute Deviation (MAD)2
Skewness-0.22678206
Sum2.0962744 × 109
Variance6.1694441
MonotonicityNot monotonic
2023-04-21T15:41:30.551562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2021 139223
13.4%
2022 137344
13.2%
2020 134699
13.0%
2019 134521
13.0%
2018 124259
12.0%
2016 102935
9.9%
2017 98353
9.5%
2015 96078
9.3%
2014 58392
5.6%
2023 12453
 
1.2%
Other values (17) 231
 
< 0.1%
ValueCountFrequency (%)
1982 1
 
< 0.1%
1983 1
 
< 0.1%
1984 1
 
< 0.1%
1989 1
 
< 0.1%
1993 1
 
< 0.1%
2000 1
 
< 0.1%
2003 3
< 0.1%
2004 1
 
< 0.1%
2005 2
< 0.1%
2006 2
< 0.1%
ValueCountFrequency (%)
2023 12453
 
1.2%
2022 137344
13.2%
2021 139223
13.4%
2020 134699
13.0%
2019 134521
13.0%
2018 124259
12.0%
2017 98353
9.5%
2016 102935
9.9%
2015 96078
9.3%
2014 58392
5.6%
Distinct12
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Memory size7.9 MiB
July
97230 
August
95927 
October
92890 
September
90888 
December
89712 
Other values (7)
571841 

Length

Max length9
Median length7
Mean length6.1667116
Min length3

Characters and Unicode

Total characters6404056
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowDecember
3rd rowDecember
4th rowDecember
5th rowDecember

Common Values

ValueCountFrequency (%)
July 97230
9.4%
August 95927
9.2%
October 92890
8.9%
September 90888
8.8%
December 89712
8.6%
January 89583
8.6%
June 89259
8.6%
November 86530
8.3%
May 82373
7.9%
March 77549
7.5%
Other values (2) 146547
14.1%

Length

2023-04-21T15:41:30.616963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
july 97230
9.4%
august 95927
9.2%
october 92890
8.9%
september 90888
8.8%
december 89712
8.6%
january 89583
8.6%
june 89259
8.6%
november 86530
8.3%
may 82373
7.9%
march 77549
7.5%
Other values (2) 146547
14.1%

Most occurring characters

ValueCountFrequency (%)
e 966909
15.1%
r 743599
 
11.6%
u 537826
 
8.4%
b 429920
 
6.7%
a 408988
 
6.4%
y 339086
 
5.3%
t 279705
 
4.4%
J 276072
 
4.3%
m 267130
 
4.2%
c 260151
 
4.1%
Other values (16) 1894670
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5365568
83.8%
Uppercase Letter 1038488
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 966909
18.0%
r 743599
13.9%
u 537826
10.0%
b 429920
8.0%
a 408988
7.6%
y 339086
 
6.3%
t 279705
 
5.2%
m 267130
 
5.0%
c 260151
 
4.8%
o 179420
 
3.3%
Other values (8) 952834
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 276072
26.6%
A 172574
16.6%
M 159922
15.4%
O 92890
 
8.9%
S 90888
 
8.8%
D 89712
 
8.6%
N 86530
 
8.3%
F 69900
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6404056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 966909
15.1%
r 743599
 
11.6%
u 537826
 
8.4%
b 429920
 
6.7%
a 408988
 
6.4%
y 339086
 
5.3%
t 279705
 
4.4%
J 276072
 
4.3%
m 267130
 
4.2%
c 260151
 
4.1%
Other values (16) 1894670
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6404056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 966909
15.1%
r 743599
 
11.6%
u 537826
 
8.4%
b 429920
 
6.7%
a 408988
 
6.4%
y 339086
 
5.3%
t 279705
 
4.4%
J 276072
 
4.3%
m 267130
 
4.2%
c 260151
 
4.1%
Other values (16) 1894670
29.6%

Day2 of the Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Memory size7.9 MiB
Mon
156087 
Fri
151195 
Sat
148234 
Sun
147591 
Tue
145825 
Other values (2)
289556 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3115464
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWed
2nd rowFri
3rd rowSun
4th rowMon
5th rowWed

Common Values

ValueCountFrequency (%)
Mon 156087
15.0%
Fri 151195
14.6%
Sat 148234
14.3%
Sun 147591
14.2%
Tue 145825
14.0%
Thu 145803
14.0%
Wed 143753
13.8%
(Missing) 15
 
< 0.1%

Length

2023-04-21T15:41:30.677674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:30.748782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mon 156087
15.0%
fri 151195
14.6%
sat 148234
14.3%
sun 147591
14.2%
tue 145825
14.0%
thu 145803
14.0%
wed 143753
13.8%

Most occurring characters

ValueCountFrequency (%)
u 439219
14.1%
n 303678
9.7%
S 295825
9.5%
T 291628
 
9.4%
e 289578
 
9.3%
M 156087
 
5.0%
o 156087
 
5.0%
F 151195
 
4.9%
r 151195
 
4.9%
i 151195
 
4.9%
Other values (5) 729777
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2076976
66.7%
Uppercase Letter 1038488
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 439219
21.1%
n 303678
14.6%
e 289578
13.9%
o 156087
 
7.5%
r 151195
 
7.3%
i 151195
 
7.3%
a 148234
 
7.1%
t 148234
 
7.1%
h 145803
 
7.0%
d 143753
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
S 295825
28.5%
T 291628
28.1%
M 156087
15.0%
F 151195
14.6%
W 143753
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3115464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 439219
14.1%
n 303678
9.7%
S 295825
9.5%
T 291628
 
9.4%
e 289578
 
9.3%
M 156087
 
5.0%
o 156087
 
5.0%
F 151195
 
4.9%
r 151195
 
4.9%
i 151195
 
4.9%
Other values (5) 729777
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3115464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 439219
14.1%
n 303678
9.7%
S 295825
9.5%
T 291628
 
9.4%
e 289578
 
9.3%
M 156087
 
5.0%
o 156087
 
5.0%
F 151195
 
4.9%
r 151195
 
4.9%
i 151195
 
4.9%
Other values (5) 729777
23.4%
Distinct1440
Distinct (%)0.1%
Missing15
Missing (%)< 0.1%
Memory size7.9 MiB
8:00
 
27324
7:00
 
22847
9:00
 
21217
10:00
 
19566
0:00
 
19309
Other values (1435)
928225 

Length

Max length5
Median length5
Mean length4.6043931
Min length4

Characters and Unicode

Total characters4781607
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7:00
2nd row15:16
3rd row8:10
4th row2:45
5th row22:21

Common Values

ValueCountFrequency (%)
8:00 27324
 
2.6%
7:00 22847
 
2.2%
9:00 21217
 
2.0%
10:00 19566
 
1.9%
0:00 19309
 
1.9%
12:00 18784
 
1.8%
6:00 15801
 
1.5%
11:00 14823
 
1.4%
17:00 14161
 
1.4%
15:00 13235
 
1.3%
Other values (1430) 851421
82.0%

Length

2023-04-21T15:41:30.819058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8:00 27324
 
2.6%
7:00 22847
 
2.2%
9:00 21217
 
2.0%
10:00 19566
 
1.9%
0:00 19309
 
1.9%
12:00 18784
 
1.8%
6:00 15801
 
1.5%
11:00 14823
 
1.4%
17:00 14161
 
1.4%
15:00 13235
 
1.3%
Other values (1430) 851421
82.0%

Most occurring characters

ValueCountFrequency (%)
0 1117875
23.4%
: 1038488
21.7%
1 751681
15.7%
2 411053
 
8.6%
3 352857
 
7.4%
5 350329
 
7.3%
4 228824
 
4.8%
8 139999
 
2.9%
7 137942
 
2.9%
9 127440
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3743119
78.3%
Other Punctuation 1038488
 
21.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1117875
29.9%
1 751681
20.1%
2 411053
 
11.0%
3 352857
 
9.4%
5 350329
 
9.4%
4 228824
 
6.1%
8 139999
 
3.7%
7 137942
 
3.7%
9 127440
 
3.4%
6 125119
 
3.3%
Other Punctuation
ValueCountFrequency (%)
: 1038488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4781607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1117875
23.4%
: 1038488
21.7%
1 751681
15.7%
2 411053
 
8.6%
3 352857
 
7.4%
5 350329
 
7.3%
4 228824
 
4.8%
8 139999
 
2.9%
7 137942
 
2.9%
9 127440
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4781607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1117875
23.4%
: 1038488
21.7%
1 751681
15.7%
2 411053
 
8.6%
3 352857
 
7.4%
5 350329
 
7.3%
4 228824
 
4.8%
8 139999
 
2.9%
7 137942
 
2.9%
9 127440
 
2.7%

Day2 of the Year
Real number (ℝ)

Distinct366
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean187.87923
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:31.027351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1100
median192
Q3277
95-th percentile348
Maximum366
Range365
Interquartile range (IQR)177

Descriptive statistics

Standard deviation104.5146
Coefficient of variation (CV)0.55628608
Kurtosis-1.1474251
Mean187.87923
Median Absolute Deviation (MAD)88
Skewness-0.091712009
Sum1.9511032 × 108
Variance10923.301
MonotonicityNot monotonic
2023-04-21T15:41:31.103971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 3324
 
0.3%
186 3301
 
0.3%
1 3270
 
0.3%
182 3252
 
0.3%
196 3249
 
0.3%
202 3244
 
0.3%
231 3235
 
0.3%
183 3232
 
0.3%
152 3216
 
0.3%
190 3207
 
0.3%
Other values (356) 1005958
96.9%
ValueCountFrequency (%)
1 3270
0.3%
2 2820
0.3%
3 3032
0.3%
4 2966
0.3%
5 3021
0.3%
6 2969
0.3%
7 2979
0.3%
8 2894
0.3%
9 2785
0.3%
10 2742
0.3%
ValueCountFrequency (%)
366 627
 
0.1%
365 2928
0.3%
364 2987
0.3%
363 2936
0.3%
362 2865
0.3%
361 2960
0.3%
360 2463
0.2%
359 2269
0.2%
358 2631
0.3%
357 2972
0.3%

Date of Report
Categorical

Distinct3600
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
00:00.0
83671 
30:00.0
 
50529
45:00.0
 
27597
15:00.0
 
24502
20:00.0
 
22766
Other values (3595)
829438 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7269521
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03:00.0
2nd row14:00.0
3rd row33:00.0
4th row47:00.0
5th row21:00.0

Common Values

ValueCountFrequency (%)
00:00.0 83671
 
8.1%
30:00.0 50529
 
4.9%
45:00.0 27597
 
2.7%
15:00.0 24502
 
2.4%
20:00.0 22766
 
2.2%
40:00.0 22049
 
2.1%
50:00.0 21997
 
2.1%
10:00.0 20126
 
1.9%
35:00.0 18131
 
1.7%
05:00.0 17190
 
1.7%
Other values (3590) 729945
70.3%

Length

2023-04-21T15:41:31.174550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00.0 83671
 
8.1%
30:00.0 50529
 
4.9%
45:00.0 27597
 
2.7%
15:00.0 24502
 
2.4%
20:00.0 22766
 
2.2%
40:00.0 22049
 
2.1%
50:00.0 21997
 
2.1%
10:00.0 20126
 
1.9%
35:00.0 18131
 
1.7%
05:00.0 17190
 
1.7%
Other values (3590) 729945
70.3%

Most occurring characters

ValueCountFrequency (%)
0 3458442
47.6%
: 1038503
 
14.3%
. 1038503
 
14.3%
5 296041
 
4.1%
3 286212
 
3.9%
4 265677
 
3.7%
1 264938
 
3.6%
2 257351
 
3.5%
6 92052
 
1.3%
8 91600
 
1.3%
Other values (2) 180202
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5192515
71.4%
Other Punctuation 2077006
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3458442
66.6%
5 296041
 
5.7%
3 286212
 
5.5%
4 265677
 
5.1%
1 264938
 
5.1%
2 257351
 
5.0%
6 92052
 
1.8%
8 91600
 
1.8%
7 91089
 
1.8%
9 89113
 
1.7%
Other Punctuation
ValueCountFrequency (%)
: 1038503
50.0%
. 1038503
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7269521
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3458442
47.6%
: 1038503
 
14.3%
. 1038503
 
14.3%
5 296041
 
4.1%
3 286212
 
3.9%
4 265677
 
3.7%
1 264938
 
3.6%
2 257351
 
3.5%
6 92052
 
1.3%
8 91600
 
1.3%
Other values (2) 180202
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7269521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3458442
47.6%
: 1038503
 
14.3%
. 1038503
 
14.3%
5 296041
 
4.1%
3 286212
 
3.9%
4 265677
 
3.7%
1 264938
 
3.6%
2 257351
 
3.5%
6 92052
 
1.3%
8 91600
 
1.3%
Other values (2) 180202
 
2.5%
Distinct3600
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
26:02.0
 
429
45:53.0
 
415
00:58.0
 
415
00:39.0
 
404
55:41.0
 
403
Other values (3595)
1036437 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7269521
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row52:49.0
2nd row54:25.0
3rd row47:45.0
4th row35:04.0
5th row10:26.0

Common Values

ValueCountFrequency (%)
26:02.0 429
 
< 0.1%
45:53.0 415
 
< 0.1%
00:58.0 415
 
< 0.1%
00:39.0 404
 
< 0.1%
55:41.0 403
 
< 0.1%
05:44.0 401
 
< 0.1%
55:59.0 395
 
< 0.1%
00:23.0 391
 
< 0.1%
40:47.0 391
 
< 0.1%
15:31.0 389
 
< 0.1%
Other values (3590) 1034470
99.6%

Length

2023-04-21T15:41:31.231365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26:02.0 429
 
< 0.1%
00:58.0 415
 
< 0.1%
45:53.0 415
 
< 0.1%
00:39.0 404
 
< 0.1%
55:41.0 403
 
< 0.1%
05:44.0 401
 
< 0.1%
55:59.0 395
 
< 0.1%
00:23.0 391
 
< 0.1%
40:47.0 391
 
< 0.1%
15:31.0 389
 
< 0.1%
Other values (3590) 1034470
99.6%

Most occurring characters

ValueCountFrequency (%)
0 1609974
22.1%
: 1038503
14.3%
. 1038503
14.3%
5 575400
 
7.9%
1 553608
 
7.6%
4 548958
 
7.6%
3 544262
 
7.5%
2 542653
 
7.5%
6 208970
 
2.9%
7 203217
 
2.8%
Other values (2) 405473
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5192515
71.4%
Other Punctuation 2077006
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1609974
31.0%
5 575400
 
11.1%
1 553608
 
10.7%
4 548958
 
10.6%
3 544262
 
10.5%
2 542653
 
10.5%
6 208970
 
4.0%
7 203217
 
3.9%
8 203066
 
3.9%
9 202407
 
3.9%
Other Punctuation
ValueCountFrequency (%)
: 1038503
50.0%
. 1038503
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7269521
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1609974
22.1%
: 1038503
14.3%
. 1038503
14.3%
5 575400
 
7.9%
1 553608
 
7.6%
4 548958
 
7.6%
3 544262
 
7.5%
2 542653
 
7.5%
6 208970
 
2.9%
7 203217
 
2.8%
Other values (2) 405473
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7269521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1609974
22.1%
: 1038503
14.3%
. 1038503
14.3%
5 575400
 
7.9%
1 553608
 
7.6%
4 548958
 
7.6%
3 544262
 
7.5%
2 542653
 
7.5%
6 208970
 
2.9%
7 203217
 
2.8%
Other values (2) 405473
 
5.6%

Offense Entered Year
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5922
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:31.285461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12017
median2019
Q32021
95-th percentile2022
Maximum2023
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.484113
Coefficient of variation (CV)0.0012306166
Kurtosis-1.0771919
Mean2018.5922
Median Absolute Deviation (MAD)2
Skewness-0.21123358
Sum2.0963141 × 109
Variance6.1708175
MonotonicityNot monotonic
2023-04-21T15:41:31.338850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2022 139012
13.4%
2021 137915
13.3%
2020 134926
13.0%
2019 134275
12.9%
2018 124059
11.9%
2016 102766
9.9%
2017 98186
9.5%
2015 96015
9.2%
2014 58208
5.6%
2023 13141
 
1.3%
ValueCountFrequency (%)
2014 58208
5.6%
2015 96015
9.2%
2016 102766
9.9%
2017 98186
9.5%
2018 124059
11.9%
2019 134275
12.9%
2020 134926
13.0%
2021 137915
13.3%
2022 139012
13.4%
2023 13141
 
1.3%
ValueCountFrequency (%)
2023 13141
 
1.3%
2022 139012
13.4%
2021 137915
13.3%
2020 134926
13.0%
2019 134275
12.9%
2018 124059
11.9%
2017 98186
9.5%
2016 102766
9.9%
2015 96015
9.2%
2014 58208
5.6%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
July
96928 
August
96191 
October
92457 
January
90901 
September
90721 
Other values (7)
571305 

Length

Max length9
Median length7
Mean length6.1682393
Min length3

Characters and Unicode

Total characters6405735
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowDecember
3rd rowDecember
4th rowDecember
5th rowDecember

Common Values

ValueCountFrequency (%)
July 96928
9.3%
August 96191
9.3%
October 92457
8.9%
January 90901
8.8%
September 90721
8.7%
December 89496
8.6%
June 89474
8.6%
November 85833
8.3%
May 82026
7.9%
March 77409
7.5%
Other values (2) 147067
14.2%

Length

2023-04-21T15:41:31.402543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
july 96928
9.3%
august 96191
9.3%
october 92457
8.9%
january 90901
8.8%
september 90721
8.7%
december 89496
8.6%
june 89474
8.6%
november 85833
8.3%
may 82026
7.9%
march 77409
7.5%
Other values (2) 147067
14.2%

Most occurring characters

ValueCountFrequency (%)
e 964880
15.1%
r 744516
 
11.6%
u 540317
 
8.4%
b 429139
 
6.7%
a 411869
 
6.4%
y 340487
 
5.3%
t 279369
 
4.4%
J 277303
 
4.3%
m 266050
 
4.2%
c 259362
 
4.0%
Other values (16) 1892443
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5367232
83.8%
Uppercase Letter 1038503
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 964880
18.0%
r 744516
13.9%
u 540317
10.1%
b 429139
8.0%
a 411869
7.7%
y 340487
 
6.3%
t 279369
 
5.2%
m 266050
 
5.0%
c 259362
 
4.8%
n 180375
 
3.4%
Other values (8) 950868
17.7%
Uppercase Letter
ValueCountFrequency (%)
J 277303
26.7%
A 172626
16.6%
M 159435
15.4%
O 92457
 
8.9%
S 90721
 
8.7%
D 89496
 
8.6%
N 85833
 
8.3%
F 70632
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 6405735
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 964880
15.1%
r 744516
 
11.6%
u 540317
 
8.4%
b 429139
 
6.7%
a 411869
 
6.4%
y 340487
 
5.3%
t 279369
 
4.4%
J 277303
 
4.3%
m 266050
 
4.2%
c 259362
 
4.0%
Other values (16) 1892443
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6405735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 964880
15.1%
r 744516
 
11.6%
u 540317
 
8.4%
b 429139
 
6.7%
a 411869
 
6.4%
y 340487
 
5.3%
t 279369
 
4.4%
J 277303
 
4.3%
m 266050
 
4.2%
c 259362
 
4.0%
Other values (16) 1892443
29.5%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Mon
163591 
Tue
153596 
Fri
150516 
Thu
149602 
Wed
149213 
Other values (2)
271985 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3115509
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWed
2nd rowFri
3rd rowSun
4th rowMon
5th rowThu

Common Values

ValueCountFrequency (%)
Mon 163591
15.8%
Tue 153596
14.8%
Fri 150516
14.5%
Thu 149602
14.4%
Wed 149213
14.4%
Sat 137832
13.3%
Sun 134153
12.9%

Length

2023-04-21T15:41:31.465316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:31.540888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mon 163591
15.8%
tue 153596
14.8%
fri 150516
14.5%
thu 149602
14.4%
wed 149213
14.4%
sat 137832
13.3%
sun 134153
12.9%

Most occurring characters

ValueCountFrequency (%)
u 437351
14.0%
T 303198
9.7%
e 302809
9.7%
n 297744
9.6%
S 271985
 
8.7%
M 163591
 
5.3%
o 163591
 
5.3%
F 150516
 
4.8%
r 150516
 
4.8%
i 150516
 
4.8%
Other values (5) 723692
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2077006
66.7%
Uppercase Letter 1038503
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 437351
21.1%
e 302809
14.6%
n 297744
14.3%
o 163591
 
7.9%
r 150516
 
7.2%
i 150516
 
7.2%
h 149602
 
7.2%
d 149213
 
7.2%
a 137832
 
6.6%
t 137832
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
T 303198
29.2%
S 271985
26.2%
M 163591
15.8%
F 150516
14.5%
W 149213
14.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3115509
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 437351
14.0%
T 303198
9.7%
e 302809
9.7%
n 297744
9.6%
S 271985
 
8.7%
M 163591
 
5.3%
o 163591
 
5.3%
F 150516
 
4.8%
r 150516
 
4.8%
i 150516
 
4.8%
Other values (5) 723692
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3115509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 437351
14.0%
T 303198
9.7%
e 302809
9.7%
n 297744
9.6%
S 271985
 
8.7%
M 163591
 
5.3%
o 163591
 
5.3%
F 150516
 
4.8%
r 150516
 
4.8%
i 150516
 
4.8%
Other values (5) 723692
23.2%
Distinct1440
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
16:55
 
1426
17:00
 
1410
17:10
 
1382
16:45
 
1372
10:00
 
1369
Other values (1435)
1031544 

Length

Max length5
Median length5
Mean length4.6653057
Min length4

Characters and Unicode

Total characters4844934
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8:52
2nd row17:54
3rd row8:47
4th row4:35
5th row1:10

Common Values

ValueCountFrequency (%)
16:55 1426
 
0.1%
17:00 1410
 
0.1%
17:10 1382
 
0.1%
16:45 1372
 
0.1%
10:00 1369
 
0.1%
16:50 1366
 
0.1%
17:05 1358
 
0.1%
16:40 1327
 
0.1%
9:55 1320
 
0.1%
10:05 1311
 
0.1%
Other values (1430) 1024862
98.7%

Length

2023-04-21T15:41:31.614192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:55 1426
 
0.1%
17:00 1410
 
0.1%
17:10 1382
 
0.1%
16:45 1372
 
0.1%
10:00 1369
 
0.1%
16:50 1366
 
0.1%
17:05 1358
 
0.1%
16:40 1327
 
0.1%
9:55 1320
 
0.1%
10:05 1311
 
0.1%
Other values (1430) 1024862
98.7%

Most occurring characters

ValueCountFrequency (%)
: 1038503
21.4%
1 955849
19.7%
2 535853
11.1%
0 443478
9.2%
3 375273
 
7.7%
5 362688
 
7.5%
4 337871
 
7.0%
9 210894
 
4.4%
8 206088
 
4.3%
7 192959
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3806431
78.6%
Other Punctuation 1038503
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 955849
25.1%
2 535853
14.1%
0 443478
11.7%
3 375273
 
9.9%
5 362688
 
9.5%
4 337871
 
8.9%
9 210894
 
5.5%
8 206088
 
5.4%
7 192959
 
5.1%
6 185478
 
4.9%
Other Punctuation
ValueCountFrequency (%)
: 1038503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4844934
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 1038503
21.4%
1 955849
19.7%
2 535853
11.1%
0 443478
9.2%
3 375273
 
7.7%
5 362688
 
7.5%
4 337871
 
7.0%
9 210894
 
4.4%
8 206088
 
4.3%
7 192959
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4844934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 1038503
21.4%
1 955849
19.7%
2 535853
11.1%
0 443478
9.2%
3 375273
 
7.7%
5 362688
 
7.5%
4 337871
 
7.0%
9 210894
 
4.4%
8 206088
 
4.3%
7 192959
 
4.0%

Offense Entered Date/Time
Real number (ℝ)

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.43087
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:31.694944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q199
median192
Q3276
95-th percentile347
Maximum366
Range365
Interquartile range (IQR)177

Descriptive statistics

Standard deviation104.62636
Coefficient of variation (CV)0.55821304
Kurtosis-1.1491735
Mean187.43087
Median Absolute Deviation (MAD)88
Skewness-0.089028288
Sum1.9464752 × 108
Variance10946.675
MonotonicityNot monotonic
2023-04-21T15:41:31.771710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237 3530
 
0.3%
226 3354
 
0.3%
284 3331
 
0.3%
192 3300
 
0.3%
183 3291
 
0.3%
273 3270
 
0.3%
252 3266
 
0.3%
190 3258
 
0.3%
228 3256
 
0.3%
231 3252
 
0.3%
Other values (356) 1005395
96.8%
ValueCountFrequency (%)
1 2817
0.3%
2 3033
0.3%
3 3238
0.3%
4 3192
0.3%
5 3213
0.3%
6 3107
0.3%
7 3070
0.3%
8 2828
0.3%
9 2854
0.3%
10 2709
0.3%
ValueCountFrequency (%)
366 580
 
0.1%
365 2932
0.3%
364 2977
0.3%
363 3123
0.3%
362 2947
0.3%
361 3006
0.3%
360 2497
0.2%
359 2068
0.2%
358 2544
0.2%
357 2879
0.3%

CFS Number
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct795822
Distinct (%)81.2%
Missing58417
Missing (%)5.6%
Memory size7.9 MiB
17-1798291
 
139
20-0979386
 
66
19-1518879
 
60
15-2011150
 
52
21-0257406
 
50
Other values (795817)
979719 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9800860
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique654397 ?
Unique (%)66.8%

Sample

1st row22-2198631
2nd row22-2536488
3rd row22-2361603
4th row21-2470552
5th row22-2434163

Common Values

ValueCountFrequency (%)
17-1798291 139
 
< 0.1%
20-0979386 66
 
< 0.1%
19-1518879 60
 
< 0.1%
15-2011150 52
 
< 0.1%
21-0257406 50
 
< 0.1%
19-2058297 46
 
< 0.1%
17-2012378 44
 
< 0.1%
15-2087629 41
 
< 0.1%
20-0450444 40
 
< 0.1%
15-1958964 40
 
< 0.1%
Other values (795812) 979508
94.3%
(Missing) 58417
 
5.6%

Length

2023-04-21T15:41:31.886588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17-1798291 139
 
< 0.1%
20-0979386 66
 
< 0.1%
19-1518879 60
 
< 0.1%
15-2011150 52
 
< 0.1%
21-0257406 50
 
< 0.1%
19-2058297 46
 
< 0.1%
17-2012378 44
 
< 0.1%
15-2087629 41
 
< 0.1%
20-0450444 40
 
< 0.1%
15-1958964 40
 
< 0.1%
Other values (795812) 979508
99.9%

Most occurring characters

ValueCountFrequency (%)
1 1750825
17.9%
2 1308873
13.4%
0 1102874
11.3%
- 980086
10.0%
9 690367
 
7.0%
8 685022
 
7.0%
5 675642
 
6.9%
6 671001
 
6.8%
7 661753
 
6.8%
4 656683
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8820774
90.0%
Dash Punctuation 980086
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1750825
19.8%
2 1308873
14.8%
0 1102874
12.5%
9 690367
 
7.8%
8 685022
 
7.8%
5 675642
 
7.7%
6 671001
 
7.6%
7 661753
 
7.5%
4 656683
 
7.4%
3 617734
 
7.0%
Dash Punctuation
ValueCountFrequency (%)
- 980086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9800860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1750825
17.9%
2 1308873
13.4%
0 1102874
11.3%
- 980086
10.0%
9 690367
 
7.0%
8 685022
 
7.0%
5 675642
 
6.9%
6 671001
 
6.8%
7 661753
 
6.8%
4 656683
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9800860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1750825
17.9%
2 1308873
13.4%
0 1102874
11.3%
- 980086
10.0%
9 690367
 
7.0%
8 685022
 
7.0%
5 675642
 
6.9%
6 671001
 
6.8%
7 661753
 
6.8%
4 656683
 
6.7%

Call Received Date Time
Categorical

HIGH CARDINALITY  MISSING 

Distinct3600
Distinct (%)0.4%
Missing58417
Missing (%)5.6%
Memory size7.9 MiB
25:13.0
 
377
24:46.0
 
368
02:33.0
 
356
44:09.0
 
356
58:08.0
 
345
Other values (3595)
978284 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6860602
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03:54.0
2nd row14:04.0
3rd row33:26.0
4th row47:00.0
5th row21:29.0

Common Values

ValueCountFrequency (%)
25:13.0 377
 
< 0.1%
24:46.0 368
 
< 0.1%
02:33.0 356
 
< 0.1%
44:09.0 356
 
< 0.1%
58:08.0 345
 
< 0.1%
59:18.0 345
 
< 0.1%
12:36.0 344
 
< 0.1%
44:06.0 342
 
< 0.1%
00:07.0 342
 
< 0.1%
48:10.0 341
 
< 0.1%
Other values (3590) 976570
94.0%
(Missing) 58417
 
5.6%

Length

2023-04-21T15:41:31.951250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25:13.0 377
 
< 0.1%
24:46.0 368
 
< 0.1%
02:33.0 356
 
< 0.1%
44:09.0 356
 
< 0.1%
58:08.0 345
 
< 0.1%
59:18.0 345
 
< 0.1%
12:36.0 344
 
< 0.1%
44:06.0 342
 
< 0.1%
00:07.0 342
 
< 0.1%
48:10.0 341
 
< 0.1%
Other values (3590) 976570
99.6%

Most occurring characters

ValueCountFrequency (%)
0 1503498
21.9%
: 980086
14.3%
. 980086
14.3%
2 523715
 
7.6%
1 523455
 
7.6%
4 522332
 
7.6%
3 522165
 
7.6%
5 521023
 
7.6%
9 196350
 
2.9%
6 196160
 
2.9%
Other values (2) 391732
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4900430
71.4%
Other Punctuation 1960172
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1503498
30.7%
2 523715
 
10.7%
1 523455
 
10.7%
4 522332
 
10.7%
3 522165
 
10.7%
5 521023
 
10.6%
9 196350
 
4.0%
6 196160
 
4.0%
7 195963
 
4.0%
8 195769
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 980086
50.0%
. 980086
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6860602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1503498
21.9%
: 980086
14.3%
. 980086
14.3%
2 523715
 
7.6%
1 523455
 
7.6%
4 522332
 
7.6%
3 522165
 
7.6%
5 521023
 
7.6%
9 196350
 
2.9%
6 196160
 
2.9%
Other values (2) 391732
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6860602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1503498
21.9%
: 980086
14.3%
. 980086
14.3%
2 523715
 
7.6%
1 523455
 
7.6%
4 522332
 
7.6%
3 522165
 
7.6%
5 521023
 
7.6%
9 196350
 
2.9%
6 196160
 
2.9%
Other values (2) 391732
 
5.7%

Call Date Time
Categorical

HIGH CARDINALITY  MISSING 

Distinct3600
Distinct (%)0.4%
Missing58417
Missing (%)5.6%
Memory size7.9 MiB
25:13.0
 
382
36:52.0
 
353
26:03.0
 
353
02:33.0
 
352
44:09.0
 
348
Other values (3595)
978298 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6860602
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03:54.0
2nd row14:03.0
3rd row33:26.0
4th row47:00.0
5th row21:29.0

Common Values

ValueCountFrequency (%)
25:13.0 382
 
< 0.1%
36:52.0 353
 
< 0.1%
26:03.0 353
 
< 0.1%
02:33.0 352
 
< 0.1%
44:09.0 348
 
< 0.1%
33:41.0 346
 
< 0.1%
44:07.0 342
 
< 0.1%
12:36.0 341
 
< 0.1%
02:17.0 341
 
< 0.1%
36:15.0 338
 
< 0.1%
Other values (3590) 976590
94.0%
(Missing) 58417
 
5.6%

Length

2023-04-21T15:41:32.007733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25:13.0 382
 
< 0.1%
26:03.0 353
 
< 0.1%
36:52.0 353
 
< 0.1%
02:33.0 352
 
< 0.1%
44:09.0 348
 
< 0.1%
33:41.0 346
 
< 0.1%
44:07.0 342
 
< 0.1%
12:36.0 341
 
< 0.1%
02:17.0 341
 
< 0.1%
36:15.0 338
 
< 0.1%
Other values (3590) 976590
99.6%

Most occurring characters

ValueCountFrequency (%)
0 1503477
21.9%
: 980086
14.3%
. 980086
14.3%
1 523617
 
7.6%
2 523598
 
7.6%
3 522279
 
7.6%
4 522229
 
7.6%
5 521251
 
7.6%
6 196369
 
2.9%
9 196279
 
2.9%
Other values (2) 391331
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4900430
71.4%
Other Punctuation 1960172
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1503477
30.7%
1 523617
 
10.7%
2 523598
 
10.7%
3 522279
 
10.7%
4 522229
 
10.7%
5 521251
 
10.6%
6 196369
 
4.0%
9 196279
 
4.0%
8 195778
 
4.0%
7 195553
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 980086
50.0%
. 980086
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6860602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1503477
21.9%
: 980086
14.3%
. 980086
14.3%
1 523617
 
7.6%
2 523598
 
7.6%
3 522279
 
7.6%
4 522229
 
7.6%
5 521251
 
7.6%
6 196369
 
2.9%
9 196279
 
2.9%
Other values (2) 391331
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6860602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1503477
21.9%
: 980086
14.3%
. 980086
14.3%
1 523617
 
7.6%
2 523598
 
7.6%
3 522279
 
7.6%
4 522229
 
7.6%
5 521251
 
7.6%
6 196369
 
2.9%
9 196279
 
2.9%
Other values (2) 391331
 
5.7%

Call Cleared Date Time
Categorical

HIGH CARDINALITY  MISSING 

Distinct3600
Distinct (%)0.4%
Missing58884
Missing (%)5.7%
Memory size7.9 MiB
45:04.0
 
718
45:02.0
 
698
45:05.0
 
656
45:03.0
 
656
45:06.0
 
625
Other values (3595)
976266 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6857333
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04:27.0
2nd row02:08.0
3rd row00:26.0
4th row34:12.0
5th row22:36.0

Common Values

ValueCountFrequency (%)
45:04.0 718
 
0.1%
45:02.0 698
 
0.1%
45:05.0 656
 
0.1%
45:03.0 656
 
0.1%
45:06.0 625
 
0.1%
45:07.0 611
 
0.1%
45:10.0 596
 
0.1%
45:09.0 585
 
0.1%
45:11.0 574
 
0.1%
45:08.0 565
 
0.1%
Other values (3590) 973335
93.7%
(Missing) 58884
 
5.7%

Length

2023-04-21T15:41:32.066287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45:04.0 718
 
0.1%
45:02.0 698
 
0.1%
45:05.0 656
 
0.1%
45:03.0 656
 
0.1%
45:06.0 625
 
0.1%
45:07.0 611
 
0.1%
45:10.0 596
 
0.1%
45:09.0 585
 
0.1%
45:11.0 574
 
0.1%
45:08.0 565
 
0.1%
Other values (3590) 973335
99.4%

Most occurring characters

ValueCountFrequency (%)
0 1498922
21.9%
: 979619
14.3%
. 979619
14.3%
4 559017
 
8.2%
5 533139
 
7.8%
3 515373
 
7.5%
1 509226
 
7.4%
2 505394
 
7.4%
6 196368
 
2.9%
8 194326
 
2.8%
Other values (2) 386330
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4898095
71.4%
Other Punctuation 1959238
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1498922
30.6%
4 559017
 
11.4%
5 533139
 
10.9%
3 515373
 
10.5%
1 509226
 
10.4%
2 505394
 
10.3%
6 196368
 
4.0%
8 194326
 
4.0%
7 193713
 
4.0%
9 192617
 
3.9%
Other Punctuation
ValueCountFrequency (%)
: 979619
50.0%
. 979619
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6857333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1498922
21.9%
: 979619
14.3%
. 979619
14.3%
4 559017
 
8.2%
5 533139
 
7.8%
3 515373
 
7.5%
1 509226
 
7.4%
2 505394
 
7.4%
6 196368
 
2.9%
8 194326
 
2.8%
Other values (2) 386330
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6857333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1498922
21.9%
: 979619
14.3%
. 979619
14.3%
4 559017
 
8.2%
5 533139
 
7.8%
3 515373
 
7.5%
1 509226
 
7.4%
2 505394
 
7.4%
6 196368
 
2.9%
8 194326
 
2.8%
Other values (2) 386330
 
5.6%

Call Dispatch Date Time
Categorical

HIGH CARDINALITY  MISSING 

Distinct3600
Distinct (%)0.4%
Missing58592
Missing (%)5.6%
Memory size7.9 MiB
25:14.0
 
415
44:09.0
 
371
23:32.0
 
366
24:46.0
 
362
36:15.0
 
360
Other values (3595)
978037 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6859377
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row37:45.0
2nd row32:58.0
3rd row39:36.0
4th row18:03.0
5th row23:59.0

Common Values

ValueCountFrequency (%)
25:14.0 415
 
< 0.1%
44:09.0 371
 
< 0.1%
23:32.0 366
 
< 0.1%
24:46.0 362
 
< 0.1%
36:15.0 360
 
< 0.1%
39:02.0 356
 
< 0.1%
30:21.0 351
 
< 0.1%
24:15.0 346
 
< 0.1%
35:18.0 344
 
< 0.1%
41:30.0 343
 
< 0.1%
Other values (3590) 976297
94.0%
(Missing) 58592
 
5.6%

Length

2023-04-21T15:41:32.122559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25:14.0 415
 
< 0.1%
44:09.0 371
 
< 0.1%
23:32.0 366
 
< 0.1%
24:46.0 362
 
< 0.1%
36:15.0 360
 
< 0.1%
39:02.0 356
 
< 0.1%
30:21.0 351
 
< 0.1%
24:15.0 346
 
< 0.1%
35:18.0 344
 
< 0.1%
41:30.0 343
 
< 0.1%
Other values (3590) 976297
99.6%

Most occurring characters

ValueCountFrequency (%)
0 1493576
21.8%
: 979911
14.3%
. 979911
14.3%
3 533368
 
7.8%
2 526657
 
7.7%
4 526207
 
7.7%
5 518360
 
7.6%
1 516092
 
7.5%
8 196703
 
2.9%
9 196658
 
2.9%
Other values (2) 391934
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4899555
71.4%
Other Punctuation 1959822
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1493576
30.5%
3 533368
 
10.9%
2 526657
 
10.7%
4 526207
 
10.7%
5 518360
 
10.6%
1 516092
 
10.5%
8 196703
 
4.0%
9 196658
 
4.0%
6 196217
 
4.0%
7 195717
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 979911
50.0%
. 979911
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6859377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1493576
21.8%
: 979911
14.3%
. 979911
14.3%
3 533368
 
7.8%
2 526657
 
7.7%
4 526207
 
7.7%
5 518360
 
7.6%
1 516092
 
7.5%
8 196703
 
2.9%
9 196658
 
2.9%
Other values (2) 391934
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6859377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1493576
21.8%
: 979911
14.3%
. 979911
14.3%
3 533368
 
7.8%
2 526657
 
7.7%
4 526207
 
7.7%
5 518360
 
7.6%
1 516092
 
7.5%
8 196703
 
2.9%
9 196658
 
2.9%
Other values (2) 391934
 
5.7%

Special Report (Pre-RMS)
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)0.4%
Missing1034297
Missing (%)99.6%
Memory size7.9 MiB
State Fair (Inside Fair)
1635 
RMS-System Dark
951 
Alan Ross Texas Freedom Parade
433 
State Fair
337 
Fireworks At River Bottoms
206 
Other values (10)
644 

Length

Max length30
Median length29
Mean length20.985735
Min length7

Characters and Unicode

Total characters88266
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDecompression Center
2nd rowDecompression Center
3rd rowRMS-System Dark
4th rowRMS-System Dark
5th rowSt. Patrick's Day Parade

Common Values

ValueCountFrequency (%)
State Fair (Inside Fair) 1635
 
0.2%
RMS-System Dark 951
 
0.1%
Alan Ross Texas Freedom Parade 433
 
< 0.1%
State Fair 337
 
< 0.1%
Fireworks At River Bottoms 206
 
< 0.1%
State Fair (Outside Fair) 146
 
< 0.1%
Protest 123
 
< 0.1%
Fireworks At State Fair 117
 
< 0.1%
Martin Luther King Jr. Parade 89
 
< 0.1%
Mary Kay Convention 48
 
< 0.1%
Other values (5) 121
 
< 0.1%
(Missing) 1034297
99.6%

Length

2023-04-21T15:41:32.185472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fair 4016
28.3%
state 2235
15.7%
inside 1635
11.5%
rms-system 951
 
6.7%
dark 951
 
6.7%
parade 562
 
4.0%
texas 433
 
3.0%
freedom 433
 
3.0%
ross 433
 
3.0%
alan 433
 
3.0%
Other values (26) 2120
14.9%

Most occurring characters

ValueCountFrequency (%)
9996
11.3%
a 9514
10.8%
e 7841
 
8.9%
r 7422
 
8.4%
t 6915
 
7.8%
i 6644
 
7.5%
s 4836
 
5.5%
F 4795
 
5.4%
S 4203
 
4.8%
d 2790
 
3.2%
Other values (34) 23310
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56430
63.9%
Uppercase Letter 17144
 
19.4%
Space Separator 9996
 
11.3%
Close Punctuation 1781
 
2.0%
Open Punctuation 1781
 
2.0%
Dash Punctuation 965
 
1.1%
Other Punctuation 169
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9514
16.9%
e 7841
13.9%
r 7422
13.2%
t 6915
12.3%
i 6644
11.8%
s 4836
8.6%
d 2790
 
4.9%
n 2466
 
4.4%
o 1924
 
3.4%
m 1628
 
2.9%
Other values (12) 4450
7.9%
Uppercase Letter
ValueCountFrequency (%)
F 4795
28.0%
S 4203
24.5%
I 1635
 
9.5%
R 1616
 
9.4%
M 1125
 
6.6%
D 1049
 
6.1%
A 756
 
4.4%
P 739
 
4.3%
T 447
 
2.6%
B 206
 
1.2%
Other values (6) 573
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 129
76.3%
' 40
 
23.7%
Space Separator
ValueCountFrequency (%)
9996
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1781
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1781
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 965
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73574
83.4%
Common 14692
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9514
12.9%
e 7841
10.7%
r 7422
10.1%
t 6915
9.4%
i 6644
9.0%
s 4836
 
6.6%
F 4795
 
6.5%
S 4203
 
5.7%
d 2790
 
3.8%
n 2466
 
3.4%
Other values (28) 16148
21.9%
Common
ValueCountFrequency (%)
9996
68.0%
) 1781
 
12.1%
( 1781
 
12.1%
- 965
 
6.6%
. 129
 
0.9%
' 40
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9996
11.3%
a 9514
10.8%
e 7841
 
8.9%
r 7422
 
8.4%
t 6915
 
7.8%
i 6644
 
7.5%
s 4836
 
5.5%
F 4795
 
5.4%
S 4203
 
4.8%
d 2790
 
3.2%
Other values (34) 23310
26.4%

Person Involvement Type
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing38053
Missing (%)3.7%
Memory size7.9 MiB
Victim
954960 
Registered Owner
 
40699
Owner
 
4223
Stln Vehicle (UUMV)
 
385
Driver
 
132
Other values (3)
 
51

Length

Max length19
Median length6
Mean length6.4078755
Min length5

Characters and Unicode

Total characters6410759
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVictim
2nd rowVictim
3rd rowVictim
4th rowVictim
5th rowVictim

Common Values

ValueCountFrequency (%)
Victim 954960
92.0%
Registered Owner 40699
 
3.9%
Owner 4223
 
0.4%
Stln Vehicle (UUMV) 385
 
< 0.1%
Driver 132
 
< 0.1%
Reporting Person 24
 
< 0.1%
Witness 17
 
< 0.1%
Passenger 10
 
< 0.1%
(Missing) 38053
 
3.7%

Length

2023-04-21T15:41:32.248853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:32.323128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
victim 954960
91.7%
owner 44922
 
4.3%
registered 40699
 
3.9%
stln 385
 
< 0.1%
vehicle 385
 
< 0.1%
uumv 385
 
< 0.1%
driver 132
 
< 0.1%
reporting 24
 
< 0.1%
person 24
 
< 0.1%
witness 17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 1951177
30.4%
t 996085
15.5%
V 955730
14.9%
c 955345
14.9%
m 954960
14.9%
e 168006
 
2.6%
r 85943
 
1.3%
n 45382
 
0.7%
O 44922
 
0.7%
w 44922
 
0.7%
Other values (19) 208287
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5325398
83.1%
Uppercase Letter 1043098
 
16.3%
Space Separator 41493
 
0.6%
Close Punctuation 385
 
< 0.1%
Open Punctuation 385
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1951177
36.6%
t 996085
18.7%
c 955345
17.9%
m 954960
17.9%
e 168006
 
3.2%
r 85943
 
1.6%
n 45382
 
0.9%
w 44922
 
0.8%
s 40777
 
0.8%
g 40733
 
0.8%
Other values (7) 42068
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
V 955730
91.6%
O 44922
 
4.3%
R 40723
 
3.9%
U 770
 
0.1%
M 385
 
< 0.1%
S 385
 
< 0.1%
D 132
 
< 0.1%
P 34
 
< 0.1%
W 17
 
< 0.1%
Space Separator
ValueCountFrequency (%)
41493
100.0%
Close Punctuation
ValueCountFrequency (%)
) 385
100.0%
Open Punctuation
ValueCountFrequency (%)
( 385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6368496
99.3%
Common 42263
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1951177
30.6%
t 996085
15.6%
V 955730
15.0%
c 955345
15.0%
m 954960
15.0%
e 168006
 
2.6%
r 85943
 
1.3%
n 45382
 
0.7%
O 44922
 
0.7%
w 44922
 
0.7%
Other values (16) 166024
 
2.6%
Common
ValueCountFrequency (%)
41493
98.2%
) 385
 
0.9%
( 385
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6410759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1951177
30.4%
t 996085
15.5%
V 955730
14.9%
c 955345
14.9%
m 954960
14.9%
e 168006
 
2.6%
r 85943
 
1.3%
n 45382
 
0.7%
O 44922
 
0.7%
w 44922
 
0.7%
Other values (19) 208287
 
3.2%

Victim Type
Categorical

IMBALANCE  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing45799
Missing (%)4.4%
Memory size7.9 MiB
Individual
657003 
Business
153653 
Government
115283 
Society/Public
 
63759
Religious Organizati
 
1412
Other values (4)
 
1594

Length

Max length20
Median length10
Mean length9.9773457
Min length5

Characters and Unicode

Total characters9904551
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowBusiness
4th rowSociety/Public
5th rowSociety/Public

Common Values

ValueCountFrequency (%)
Individual 657003
63.3%
Business 153653
 
14.8%
Government 115283
 
11.1%
Society/Public 63759
 
6.1%
Religious Organizati 1412
 
0.1%
Financial Institutio 1057
 
0.1%
Law Enforcement Offi 518
 
< 0.1%
Other 16
 
< 0.1%
Unknown 3
 
< 0.1%
(Missing) 45799
 
4.4%

Length

2023-04-21T15:41:32.392947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:32.468751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
individual 657003
66.0%
business 153653
 
15.4%
government 115283
 
11.6%
society/public 63759
 
6.4%
religious 1412
 
0.1%
organizati 1412
 
0.1%
financial 1057
 
0.1%
institutio 1057
 
0.1%
law 518
 
0.1%
enforcement 518
 
0.1%
Other values (3) 537
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 1605571
16.2%
d 1314006
13.3%
n 1046850
10.6%
u 876884
8.9%
v 772286
7.8%
l 723231
7.3%
a 662459
6.7%
I 658060
6.6%
s 463428
 
4.7%
e 450442
 
4.5%
Other values (25) 1331334
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8777319
88.6%
Uppercase Letter 1059968
 
10.7%
Other Punctuation 63759
 
0.6%
Space Separator 3505
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1605571
18.3%
d 1314006
15.0%
n 1046850
11.9%
u 876884
10.0%
v 772286
8.8%
l 723231
8.2%
a 662459
7.5%
s 463428
 
5.3%
e 450442
 
5.1%
t 184159
 
2.1%
Other values (12) 678003
7.7%
Uppercase Letter
ValueCountFrequency (%)
I 658060
62.1%
B 153653
 
14.5%
G 115283
 
10.9%
S 63759
 
6.0%
P 63759
 
6.0%
O 1946
 
0.2%
R 1412
 
0.1%
F 1057
 
0.1%
L 518
 
< 0.1%
E 518
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 63759
100.0%
Space Separator
ValueCountFrequency (%)
3505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9837287
99.3%
Common 67264
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1605571
16.3%
d 1314006
13.4%
n 1046850
10.6%
u 876884
8.9%
v 772286
7.9%
l 723231
7.4%
a 662459
6.7%
I 658060
6.7%
s 463428
 
4.7%
e 450442
 
4.6%
Other values (23) 1264070
12.8%
Common
ValueCountFrequency (%)
/ 63759
94.8%
3505
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9904551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1605571
16.2%
d 1314006
13.3%
n 1046850
10.6%
u 876884
8.9%
v 772286
7.8%
l 723231
7.3%
a 662459
6.7%
I 658060
6.6%
s 463428
 
4.7%
e 450442
 
4.5%
Other values (25) 1331334
13.4%

Victim Race
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing374754
Missing (%)36.1%
Memory size7.9 MiB
Black
218245 
Hispanic or Latino
209688 
White
204623 
Asian
 
12066
Middle Eastern
 
7652
Other values (7)
 
11475

Length

Max length32
Median length5
Mean length9.3916963
Min length1

Characters and Unicode

Total characters6233729
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHispanic or Latino
2nd rowWhite
3rd rowBlack
4th rowHispanic or Latino
5th rowHispanic or Latino

Common Values

ValueCountFrequency (%)
Black 218245
21.0%
Hispanic or Latino 209688
20.2%
White 204623
19.7%
Asian 12066
 
1.2%
Middle Eastern 7652
 
0.7%
Unknown 6065
 
0.6%
Non-Hispanic or Latino 3392
 
0.3%
American Indian or Alaska Native 982
 
0.1%
Native Hawaiian/Pacific Islander 899
 
0.1%
NH 115
 
< 0.1%
Other values (2) 22
 
< 0.1%
(Missing) 374754
36.1%

Length

2023-04-21T15:41:32.565025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 218245
19.8%
or 214062
19.4%
latino 213080
19.3%
hispanic 209688
19.0%
white 204623
18.5%
asian 12066
 
1.1%
middle 7652
 
0.7%
eastern 7652
 
0.7%
unknown 6065
 
0.5%
non-hispanic 3392
 
0.3%
Other values (9) 6762
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 871022
14.0%
a 674427
 
10.8%
n 472209
 
7.6%
439538
 
7.1%
o 436599
 
7.0%
c 434105
 
7.0%
t 427236
 
6.9%
s 234679
 
3.8%
l 227778
 
3.7%
k 225292
 
3.6%
Other values (24) 1790844
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4896245
78.5%
Uppercase Letter 893655
 
14.3%
Space Separator 439538
 
7.1%
Dash Punctuation 3392
 
0.1%
Other Punctuation 899
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 871022
17.8%
a 674427
13.8%
n 472209
9.6%
o 436599
8.9%
c 434105
8.9%
t 427236
8.7%
s 234679
 
4.8%
l 227778
 
4.7%
k 225292
 
4.6%
e 223689
 
4.6%
Other values (8) 669209
13.7%
Uppercase Letter
ValueCountFrequency (%)
B 218245
24.4%
H 214108
24.0%
L 213080
23.8%
W 204623
22.9%
A 14030
 
1.6%
E 7660
 
0.9%
M 7652
 
0.9%
U 6065
 
0.7%
N 5388
 
0.6%
I 1881
 
0.2%
Other values (3) 923
 
0.1%
Space Separator
ValueCountFrequency (%)
439538
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3392
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5789900
92.9%
Common 443829
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 871022
15.0%
a 674427
11.6%
n 472209
 
8.2%
o 436599
 
7.5%
c 434105
 
7.5%
t 427236
 
7.4%
s 234679
 
4.1%
l 227778
 
3.9%
k 225292
 
3.9%
e 223689
 
3.9%
Other values (21) 1562864
27.0%
Common
ValueCountFrequency (%)
439538
99.0%
- 3392
 
0.8%
/ 899
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6233729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 871022
14.0%
a 674427
 
10.8%
n 472209
 
7.6%
439538
 
7.1%
o 436599
 
7.0%
c 434105
 
7.0%
t 427236
 
6.9%
s 234679
 
3.8%
l 227778
 
3.7%
k 225292
 
3.6%
Other values (24) 1790844
28.7%

Victim Ethnicity
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing375735
Missing (%)36.2%
Memory size7.9 MiB
Non-Hispanic or Latino
454989 
Hispanic or Latino
205353 
Unknown
 
2426

Length

Max length22
Median length22
Mean length20.705728
Min length7

Characters and Unicode

Total characters13723094
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHispanic or Latino
2nd rowNon-Hispanic or Latino
3rd rowNon-Hispanic or Latino
4th rowHispanic or Latino
5th rowHispanic or Latino

Common Values

ValueCountFrequency (%)
Non-Hispanic or Latino 454989
43.8%
Hispanic or Latino 205353
19.8%
Unknown 2426
 
0.2%
(Missing) 375735
36.2%

Length

2023-04-21T15:41:32.630571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:32.700234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
or 660342
33.3%
latino 660342
33.3%
non-hispanic 454989
22.9%
hispanic 205353
 
10.4%
unknown 2426
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 1981026
14.4%
n 1782951
13.0%
o 1778099
13.0%
a 1320684
9.6%
1320684
9.6%
L 660342
 
4.8%
t 660342
 
4.8%
H 660342
 
4.8%
s 660342
 
4.8%
p 660342
 
4.8%
Other values (7) 2237940
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10169322
74.1%
Uppercase Letter 1778099
 
13.0%
Space Separator 1320684
 
9.6%
Dash Punctuation 454989
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1981026
19.5%
n 1782951
17.5%
o 1778099
17.5%
a 1320684
13.0%
t 660342
 
6.5%
s 660342
 
6.5%
p 660342
 
6.5%
c 660342
 
6.5%
r 660342
 
6.5%
k 2426
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
L 660342
37.1%
H 660342
37.1%
N 454989
25.6%
U 2426
 
0.1%
Space Separator
ValueCountFrequency (%)
1320684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 454989
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11947421
87.1%
Common 1775673
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1981026
16.6%
n 1782951
14.9%
o 1778099
14.9%
a 1320684
11.1%
L 660342
 
5.5%
t 660342
 
5.5%
H 660342
 
5.5%
s 660342
 
5.5%
p 660342
 
5.5%
c 660342
 
5.5%
Other values (5) 1122609
9.4%
Common
ValueCountFrequency (%)
1320684
74.4%
- 454989
 
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13723094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1981026
14.4%
n 1782951
13.0%
o 1778099
13.0%
a 1320684
9.6%
1320684
9.6%
L 660342
 
4.8%
t 660342
 
4.8%
H 660342
 
4.8%
s 660342
 
4.8%
p 660342
 
4.8%
Other values (7) 2237940
16.3%

Victim Gender
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing377881
Missing (%)36.4%
Memory size7.9 MiB
Male
375503 
Female
282716 
Unknown
 
2393
TEST
 
9
N
 
1

Length

Max length7
Median length4
Mean length4.866771
Min length1

Characters and Unicode

Total characters3215096
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 375503
36.2%
Female 282716
27.2%
Unknown 2393
 
0.2%
TEST 9
 
< 0.1%
N 1
 
< 0.1%
(Missing) 377881
36.4%

Length

2023-04-21T15:41:32.760228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:32.831776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 375503
56.8%
female 282716
42.8%
unknown 2393
 
0.4%
test 9
 
< 0.1%
n 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 940935
29.3%
a 658219
20.5%
l 658219
20.5%
M 375503
 
11.7%
F 282716
 
8.8%
m 282716
 
8.8%
n 7179
 
0.2%
U 2393
 
0.1%
k 2393
 
0.1%
o 2393
 
0.1%
Other values (5) 2430
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2554447
79.5%
Uppercase Letter 660649
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 940935
36.8%
a 658219
25.8%
l 658219
25.8%
m 282716
 
11.1%
n 7179
 
0.3%
k 2393
 
0.1%
o 2393
 
0.1%
w 2393
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
M 375503
56.8%
F 282716
42.8%
U 2393
 
0.4%
T 18
 
< 0.1%
E 9
 
< 0.1%
S 9
 
< 0.1%
N 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3215096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 940935
29.3%
a 658219
20.5%
l 658219
20.5%
M 375503
 
11.7%
F 282716
 
8.8%
m 282716
 
8.8%
n 7179
 
0.2%
U 2393
 
0.1%
k 2393
 
0.1%
o 2393
 
0.1%
Other values (5) 2430
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3215096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 940935
29.3%
a 658219
20.5%
l 658219
20.5%
M 375503
 
11.7%
F 282716
 
8.8%
m 282716
 
8.8%
n 7179
 
0.2%
U 2393
 
0.1%
k 2393
 
0.1%
o 2393
 
0.1%
Other values (5) 2430
 
0.1%

Responding Officer #1 Badge No
Categorical

HIGH CARDINALITY  MISSING 

Distinct4642
Distinct (%)0.5%
Missing60790
Missing (%)5.9%
Memory size7.9 MiB
94392
 
12054
118918
 
4238
106291
 
3528
120365
 
3242
6751
 
2982
Other values (4637)
951669 

Length

Max length9
Median length5
Mean length4.5975864
Min length3

Characters and Unicode

Total characters4495120
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206 ?
Unique (%)< 0.1%

Sample

1st row94392
2nd row9704
3rd row10170
4th row11935
5th row9074

Common Values

ValueCountFrequency (%)
94392 12054
 
1.2%
118918 4238
 
0.4%
106291 3528
 
0.3%
120365 3242
 
0.3%
6751 2982
 
0.3%
122756 2729
 
0.3%
129062 2397
 
0.2%
122187 2184
 
0.2%
54654 2059
 
0.2%
9347 2026
 
0.2%
Other values (4632) 940274
90.5%
(Missing) 60790
 
5.9%

Length

2023-04-21T15:41:32.895006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
94392 12054
 
1.2%
118918 4238
 
0.4%
106291 3528
 
0.4%
120365 3242
 
0.3%
6751 2982
 
0.3%
122756 2729
 
0.3%
129062 2397
 
0.2%
122187 2184
 
0.2%
54654 2059
 
0.2%
9347 2026
 
0.2%
Other values (4631) 940304
96.2%

Most occurring characters

ValueCountFrequency (%)
1 1068695
23.8%
0 533595
11.9%
9 463010
10.3%
8 397496
 
8.8%
7 372871
 
8.3%
6 356105
 
7.9%
2 354194
 
7.9%
5 323540
 
7.2%
3 310687
 
6.9%
4 305447
 
6.8%
Other values (11) 9480
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4485640
99.8%
Uppercase Letter 9208
 
0.2%
Dash Punctuation 242
 
< 0.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1068695
23.8%
0 533595
11.9%
9 463010
10.3%
8 397496
 
8.9%
7 372871
 
8.3%
6 356105
 
7.9%
2 354194
 
7.9%
5 323540
 
7.2%
3 310687
 
6.9%
4 305447
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
M 3499
38.0%
F 1358
 
14.7%
T 1129
 
12.3%
P 1023
 
11.1%
R 904
 
9.8%
U 846
 
9.2%
D 435
 
4.7%
W 11
 
0.1%
E 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 242
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4485912
99.8%
Latin 9208
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1068695
23.8%
0 533595
11.9%
9 463010
10.3%
8 397496
 
8.9%
7 372871
 
8.3%
6 356105
 
7.9%
2 354194
 
7.9%
5 323540
 
7.2%
3 310687
 
6.9%
4 305447
 
6.8%
Other values (2) 272
 
< 0.1%
Latin
ValueCountFrequency (%)
M 3499
38.0%
F 1358
 
14.7%
T 1129
 
12.3%
P 1023
 
11.1%
R 904
 
9.8%
U 846
 
9.2%
D 435
 
4.7%
W 11
 
0.1%
E 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4495120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1068695
23.8%
0 533595
11.9%
9 463010
10.3%
8 397496
 
8.8%
7 372871
 
8.3%
6 356105
 
7.9%
2 354194
 
7.9%
5 323540
 
7.2%
3 310687
 
6.9%
4 305447
 
6.8%
Other values (11) 9480
 
0.2%

Responding Officer #1 Name
Categorical

HIGH CARDINALITY  MISSING 

Distinct4579
Distinct (%)0.5%
Missing61672
Missing (%)5.9%
Memory size7.9 MiB
WILLIS,LINDA,M
 
12054
SPURR,RUTH
 
4238
BELAYE,DIANE,KAY
 
3528
BURNETT,MICHELLE,J
 
3242
CAMPOPIANO III,PAUL,PASQUELE
 
2982
Other values (4574)
950787 

Length

Max length35
Median length28
Mean length17.221466
Min length1

Characters and Unicode

Total characters16822462
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)< 0.1%

Sample

1st rowWILLIS,LINDA,M
2nd rowSTUARD,JC
3rd rowKUSCHEL,ADAM,SCOTT
4th rowDILLARD,FREEMAN,D
5th rowPULLIAM,JEREMY,DEAN

Common Values

ValueCountFrequency (%)
WILLIS,LINDA,M 12054
 
1.2%
SPURR,RUTH 4238
 
0.4%
BELAYE,DIANE,KAY 3528
 
0.3%
BURNETT,MICHELLE,J 3242
 
0.3%
CAMPOPIANO III,PAUL,PASQUELE 2982
 
0.3%
HORTON,JONQUIL 2729
 
0.3%
HANNA,MARIA 2397
 
0.2%
BARGY,TRACEY 2184
 
0.2%
MARTELL,EVA 2059
 
0.2%
NEVAREZ,FRANCISCO,JAVIER 2026
 
0.2%
Other values (4569) 939392
90.5%
(Missing) 61672
 
5.9%

Length

2023-04-21T15:41:32.972518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
willis,linda,m 12054
 
1.2%
spurr,ruth 4238
 
0.4%
belaye,diane,kay 3528
 
0.3%
burnett,michelle,j 3242
 
0.3%
campopiano 2982
 
0.3%
iii,paul,pasquele 2982
 
0.3%
horton,jonquil 2729
 
0.3%
hanna,maria 2397
 
0.2%
bargy,tracey 2184
 
0.2%
la 2175
 
0.2%
Other values (4794) 995797
96.3%

Most occurring characters

ValueCountFrequency (%)
A 1737521
 
10.3%
E 1599800
 
9.5%
, 1581064
 
9.4%
R 1317364
 
7.8%
N 1236550
 
7.4%
I 1025897
 
6.1%
L 981864
 
5.8%
O 960281
 
5.7%
S 800037
 
4.8%
T 672959
 
4.0%
Other values (24) 4909125
29.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15151908
90.1%
Other Punctuation 1599134
 
9.5%
Space Separator 57477
 
0.3%
Dash Punctuation 13454
 
0.1%
Decimal Number 489
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1737521
11.5%
E 1599800
 
10.6%
R 1317364
 
8.7%
N 1236550
 
8.2%
I 1025897
 
6.8%
L 981864
 
6.5%
O 960281
 
6.3%
S 800037
 
5.3%
T 672959
 
4.4%
H 591484
 
3.9%
Other values (16) 4228151
27.9%
Other Punctuation
ValueCountFrequency (%)
, 1581064
98.9%
. 13599
 
0.9%
' 4471
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 470
96.1%
1 16
 
3.3%
4 3
 
0.6%
Space Separator
ValueCountFrequency (%)
57477
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15151908
90.1%
Common 1670554
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1737521
11.5%
E 1599800
 
10.6%
R 1317364
 
8.7%
N 1236550
 
8.2%
I 1025897
 
6.8%
L 981864
 
6.5%
O 960281
 
6.3%
S 800037
 
5.3%
T 672959
 
4.4%
H 591484
 
3.9%
Other values (16) 4228151
27.9%
Common
ValueCountFrequency (%)
, 1581064
94.6%
57477
 
3.4%
. 13599
 
0.8%
- 13454
 
0.8%
' 4471
 
0.3%
2 470
 
< 0.1%
1 16
 
< 0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16822462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1737521
 
10.3%
E 1599800
 
9.5%
, 1581064
 
9.4%
R 1317364
 
7.8%
N 1236550
 
7.4%
I 1025897
 
6.1%
L 981864
 
5.8%
O 960281
 
5.7%
S 800037
 
4.8%
T 672959
 
4.0%
Other values (24) 4909125
29.2%

Responding Officer #2 Badge No
Categorical

HIGH CARDINALITY  MISSING 

Distinct4644
Distinct (%)1.3%
Missing691363
Missing (%)66.6%
Memory size7.9 MiB
6614
 
1124
8291
 
952
10680
 
897
9480
 
894
6700
 
888
Other values (4639)
342385 

Length

Max length9
Median length5
Mean length4.5626779
Min length3

Characters and Unicode

Total characters1583888
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique443 ?
Unique (%)0.1%

Sample

1st row10917
2nd row11632
3rd row9716
4th row12352
5th row7211

Common Values

ValueCountFrequency (%)
6614 1124
 
0.1%
8291 952
 
0.1%
10680 897
 
0.1%
9480 894
 
0.1%
6700 888
 
0.1%
8377 818
 
0.1%
9241 763
 
0.1%
9251 750
 
0.1%
5946 740
 
0.1%
8315 726
 
0.1%
Other values (4634) 338588
32.6%
(Missing) 691363
66.6%

Length

2023-04-21T15:41:33.045672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6614 1124
 
0.3%
8291 952
 
0.3%
10680 897
 
0.3%
9480 894
 
0.3%
6700 888
 
0.3%
8377 818
 
0.2%
9241 763
 
0.2%
9251 750
 
0.2%
5946 740
 
0.2%
8315 726
 
0.2%
Other values (4628) 338610
97.5%

Most occurring characters

ValueCountFrequency (%)
1 386757
24.4%
0 198341
12.5%
9 164304
10.4%
8 139030
 
8.8%
7 124692
 
7.9%
6 123466
 
7.8%
5 115761
 
7.3%
2 112831
 
7.1%
4 110422
 
7.0%
3 106071
 
6.7%
Other values (14) 2213
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1581675
99.9%
Uppercase Letter 2145
 
0.1%
Dash Punctuation 46
 
< 0.1%
Space Separator 22
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1034
48.2%
F 542
25.3%
R 412
 
19.2%
D 67
 
3.1%
W 30
 
1.4%
T 26
 
1.2%
S 16
 
0.7%
I 10
 
0.5%
A 3
 
0.1%
N 2
 
0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 386757
24.5%
0 198341
12.5%
9 164304
10.4%
8 139030
 
8.8%
7 124692
 
7.9%
6 123466
 
7.8%
5 115761
 
7.3%
2 112831
 
7.1%
4 110422
 
7.0%
3 106071
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1581743
99.9%
Latin 2145
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 386757
24.5%
0 198341
12.5%
9 164304
10.4%
8 139030
 
8.8%
7 124692
 
7.9%
6 123466
 
7.8%
5 115761
 
7.3%
2 112831
 
7.1%
4 110422
 
7.0%
3 106071
 
6.7%
Other values (2) 68
 
< 0.1%
Latin
ValueCountFrequency (%)
M 1034
48.2%
F 542
25.3%
R 412
 
19.2%
D 67
 
3.1%
W 30
 
1.4%
T 26
 
1.2%
S 16
 
0.7%
I 10
 
0.5%
A 3
 
0.1%
N 2
 
0.1%
Other values (2) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1583888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 386757
24.4%
0 198341
12.5%
9 164304
10.4%
8 139030
 
8.8%
7 124692
 
7.9%
6 123466
 
7.8%
5 115761
 
7.3%
2 112831
 
7.1%
4 110422
 
7.0%
3 106071
 
6.7%
Other values (14) 2213
 
0.1%

Responding Officer #2 Name
Categorical

HIGH CARDINALITY  MISSING 

Distinct4620
Distinct (%)1.3%
Missing691365
Missing (%)66.6%
Memory size7.9 MiB
WILKERSON,ROBERT,C
 
1124
SUVANNACHAKKHAM,SOUBIN
 
952
ORTIZ,DIEGO
 
897
DICKSON,JAY,CAMDAN
 
894
FRANCIS JR,GEORGE
 
888
Other values (4615)
342383 

Length

Max length37
Median length30
Mean length17.357014
Min length1

Characters and Unicode

Total characters6025279
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)0.1%

Sample

1st rowSTINSON,TIMOTHY
2nd rowMARSHALL,AUSTIN,TANNER
3rd rowWOMACK,VALERIE,GAIL
4th rowRIDDLE,AUSTIN
5th rowPEREZ,ROBERTO

Common Values

ValueCountFrequency (%)
WILKERSON,ROBERT,C 1124
 
0.1%
SUVANNACHAKKHAM,SOUBIN 952
 
0.1%
ORTIZ,DIEGO 897
 
0.1%
DICKSON,JAY,CAMDAN 894
 
0.1%
FRANCIS JR,GEORGE 888
 
0.1%
ELLIS,BRADLEY,C 818
 
0.1%
CARNEY,MARY,KATHRYN 763
 
0.1%
BLALOCK,MARK,CHARLES 750
 
0.1%
FLORES III,LUCAS 740
 
0.1%
GEISSLER,KEITH,THOMAS 726
 
0.1%
Other values (4610) 338586
32.6%
(Missing) 691365
66.6%

Length

2023-04-21T15:41:33.120975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la 1299
 
0.4%
wilkerson,robert,c 1124
 
0.3%
de 968
 
0.3%
suvannachakkham,soubin 952
 
0.3%
jr,george 943
 
0.3%
ortiz,diego 897
 
0.2%
dickson,jay,camdan 894
 
0.2%
francis 888
 
0.2%
ellis,bradley,c 818
 
0.2%
carney,mary,kathryn 763
 
0.2%
Other values (4843) 359084
97.4%

Most occurring characters

ValueCountFrequency (%)
A 622043
 
10.3%
E 575713
 
9.6%
, 562166
 
9.3%
R 478430
 
7.9%
N 438588
 
7.3%
O 360146
 
6.0%
I 353281
 
5.9%
L 347826
 
5.8%
S 288753
 
4.8%
T 239457
 
4.0%
Other values (22) 1758876
29.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5429882
90.1%
Other Punctuation 569404
 
9.5%
Space Separator 21492
 
0.4%
Dash Punctuation 3848
 
0.1%
Decimal Number 653
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 622043
11.5%
E 575713
 
10.6%
R 478430
 
8.8%
N 438588
 
8.1%
O 360146
 
6.6%
I 353281
 
6.5%
L 347826
 
6.4%
S 288753
 
5.3%
T 239457
 
4.4%
H 209011
 
3.8%
Other values (16) 1516634
27.9%
Other Punctuation
ValueCountFrequency (%)
, 562166
98.7%
. 7195
 
1.3%
' 43
 
< 0.1%
Space Separator
ValueCountFrequency (%)
21492
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3848
100.0%
Decimal Number
ValueCountFrequency (%)
2 653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5429882
90.1%
Common 595397
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 622043
11.5%
E 575713
 
10.6%
R 478430
 
8.8%
N 438588
 
8.1%
O 360146
 
6.6%
I 353281
 
6.5%
L 347826
 
6.4%
S 288753
 
5.3%
T 239457
 
4.4%
H 209011
 
3.8%
Other values (16) 1516634
27.9%
Common
ValueCountFrequency (%)
, 562166
94.4%
21492
 
3.6%
. 7195
 
1.2%
- 3848
 
0.6%
2 653
 
0.1%
' 43
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6025279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 622043
 
10.3%
E 575713
 
9.6%
, 562166
 
9.3%
R 478430
 
7.9%
N 438588
 
7.3%
O 360146
 
6.0%
I 353281
 
5.9%
L 347826
 
5.8%
S 288753
 
4.8%
T 239457
 
4.0%
Other values (22) 1758876
29.2%

Reporting Officer Badge No
Categorical

HIGH CARDINALITY  MISSING 

Distinct4700
Distinct (%)0.5%
Missing58567
Missing (%)5.6%
Memory size7.9 MiB
94392
 
12069
118918
 
4246
106291
 
3534
120365
 
3247
6751
 
2986
Other values (4695)
953854 

Length

Max length9
Median length5
Mean length4.5974962
Min length3

Characters and Unicode

Total characters4505252
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)< 0.1%

Sample

1st row94392
2nd row9704
3rd row10170
4th row11935
5th row9074

Common Values

ValueCountFrequency (%)
94392 12069
 
1.2%
118918 4246
 
0.4%
106291 3534
 
0.3%
120365 3247
 
0.3%
6751 2986
 
0.3%
122756 2730
 
0.3%
129062 2397
 
0.2%
122187 2184
 
0.2%
54654 2062
 
0.2%
9347 2026
 
0.2%
Other values (4690) 942455
90.8%
(Missing) 58567
 
5.6%

Length

2023-04-21T15:41:33.193897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
94392 12069
 
1.2%
118918 4246
 
0.4%
106291 3534
 
0.4%
120365 3247
 
0.3%
6751 2986
 
0.3%
122756 2730
 
0.3%
129062 2397
 
0.2%
122187 2184
 
0.2%
54654 2062
 
0.2%
9347 2026
 
0.2%
Other values (4689) 942485
96.2%

Most occurring characters

ValueCountFrequency (%)
1 1070937
23.8%
0 534804
11.9%
9 464213
10.3%
8 398362
 
8.8%
7 373678
 
8.3%
6 356857
 
7.9%
2 354965
 
7.9%
5 324314
 
7.2%
3 311390
 
6.9%
4 306174
 
6.8%
Other values (13) 9558
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4495694
99.8%
Uppercase Letter 9286
 
0.2%
Dash Punctuation 242
 
< 0.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3504
37.7%
F 1359
 
14.6%
T 1130
 
12.2%
P 1024
 
11.0%
R 909
 
9.8%
U 846
 
9.1%
D 436
 
4.7%
X 59
 
0.6%
W 11
 
0.1%
N 5
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1070937
23.8%
0 534804
11.9%
9 464213
10.3%
8 398362
 
8.9%
7 373678
 
8.3%
6 356857
 
7.9%
2 354965
 
7.9%
5 324314
 
7.2%
3 311390
 
6.9%
4 306174
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 242
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4495966
99.8%
Latin 9286
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1070937
23.8%
0 534804
11.9%
9 464213
10.3%
8 398362
 
8.9%
7 373678
 
8.3%
6 356857
 
7.9%
2 354965
 
7.9%
5 324314
 
7.2%
3 311390
 
6.9%
4 306174
 
6.8%
Other values (2) 272
 
< 0.1%
Latin
ValueCountFrequency (%)
M 3504
37.7%
F 1359
 
14.6%
T 1130
 
12.2%
P 1024
 
11.0%
R 909
 
9.8%
U 846
 
9.1%
D 436
 
4.7%
X 59
 
0.6%
W 11
 
0.1%
N 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4505252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1070937
23.8%
0 534804
11.9%
9 464213
10.3%
8 398362
 
8.8%
7 373678
 
8.3%
6 356857
 
7.9%
2 354965
 
7.9%
5 324314
 
7.2%
3 311390
 
6.9%
4 306174
 
6.8%
Other values (13) 9558
 
0.2%

Assisting Officer Badge No
Categorical

HIGH CARDINALITY  MISSING 

Distinct2077
Distinct (%)0.3%
Missing248151
Missing (%)23.9%
Memory size7.9 MiB
T168
 
38439
T270
 
23171
113456
 
17558
T187
 
15521
5799
 
11128
Other values (2072)
684535 

Length

Max length6
Median length4
Mean length4.1901583
Min length4

Characters and Unicode

Total characters3311700
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique615 ?
Unique (%)0.1%

Sample

1st rowT1245
2nd row8776
3rd row10730
4th row10774
5th rowT270

Common Values

ValueCountFrequency (%)
T168 38439
 
3.7%
T270 23171
 
2.2%
113456 17558
 
1.7%
T187 15521
 
1.5%
5799 11128
 
1.1%
T276 10222
 
1.0%
125759 9578
 
0.9%
T161 9304
 
0.9%
8219 8108
 
0.8%
8097 7811
 
0.8%
Other values (2067) 639512
61.6%
(Missing) 248151
 
23.9%

Length

2023-04-21T15:41:33.262492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t168 38439
 
4.9%
t270 23171
 
2.9%
113456 17558
 
2.2%
t187 15521
 
2.0%
5799 11128
 
1.4%
t276 10222
 
1.3%
125759 9578
 
1.2%
t161 9304
 
1.2%
8219 8108
 
1.0%
8097 7811
 
1.0%
Other values (2067) 639512
80.9%

Most occurring characters

ValueCountFrequency (%)
7 430080
13.0%
1 426922
12.9%
8 383275
11.6%
6 369410
11.2%
9 337892
10.2%
5 314525
9.5%
2 277751
8.4%
0 246242
7.4%
4 202654
6.1%
3 181132
5.5%
Other values (5) 141817
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3169883
95.7%
Uppercase Letter 141817
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 430080
13.6%
1 426922
13.5%
8 383275
12.1%
6 369410
11.7%
9 337892
10.7%
5 314525
9.9%
2 277751
8.8%
0 246242
7.8%
4 202654
6.4%
3 181132
5.7%
Uppercase Letter
ValueCountFrequency (%)
T 134434
94.8%
D 5275
 
3.7%
M 1069
 
0.8%
F 1007
 
0.7%
R 32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3169883
95.7%
Latin 141817
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
7 430080
13.6%
1 426922
13.5%
8 383275
12.1%
6 369410
11.7%
9 337892
10.7%
5 314525
9.9%
2 277751
8.8%
0 246242
7.8%
4 202654
6.4%
3 181132
5.7%
Latin
ValueCountFrequency (%)
T 134434
94.8%
D 5275
 
3.7%
M 1069
 
0.8%
F 1007
 
0.7%
R 32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3311700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 430080
13.0%
1 426922
12.9%
8 383275
11.6%
6 369410
11.2%
9 337892
10.2%
5 314525
9.5%
2 277751
8.4%
0 246242
7.4%
4 202654
6.1%
3 181132
5.5%
Other values (5) 141817
 
4.3%
Distinct255
Distinct (%)< 0.1%
Missing2860
Missing (%)0.3%
Memory size7.9 MiB
81075
83455 
15356
 
62477
111210
 
52477
118918
 
48035
57074
 
47550
Other values (250)
741649 

Length

Max length7
Median length6
Mean length5.371714
Min length1

Characters and Unicode

Total characters5563178
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st row54654
2nd row117512
3rd row15356
4th row106291
5th row106291

Common Values

ValueCountFrequency (%)
81075 83455
 
8.0%
15356 62477
 
6.0%
111210 52477
 
5.1%
118918 48035
 
4.6%
57074 47550
 
4.6%
70495 40110
 
3.9%
36201 38475
 
3.7%
106845 36998
 
3.6%
105273 31754
 
3.1%
77397 30918
 
3.0%
Other values (245) 563394
54.3%

Length

2023-04-21T15:41:33.332618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81075 83455
 
8.1%
15356 62477
 
6.0%
111210 52477
 
5.1%
118918 48035
 
4.6%
57074 47550
 
4.6%
70495 40110
 
3.9%
36201 38475
 
3.7%
106845 36998
 
3.6%
105273 31754
 
3.1%
77397 30918
 
3.0%
Other values (245) 563394
54.4%

Most occurring characters

ValueCountFrequency (%)
1 1339025
24.1%
0 637773
11.5%
7 595399
10.7%
5 567327
10.2%
3 509489
 
9.2%
2 466916
 
8.4%
8 375565
 
6.8%
9 364501
 
6.6%
4 352742
 
6.3%
6 345250
 
6.2%
Other values (8) 9191
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5553987
99.8%
Uppercase Letter 7569
 
0.1%
Dash Punctuation 1622
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1339025
24.1%
0 637773
11.5%
7 595399
10.7%
5 567327
10.2%
3 509489
 
9.2%
2 466916
 
8.4%
8 375565
 
6.8%
9 364501
 
6.6%
4 352742
 
6.4%
6 345250
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
M 7504
99.1%
N 42
 
0.6%
T 16
 
0.2%
S 2
 
< 0.1%
U 2
 
< 0.1%
P 2
 
< 0.1%
O 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5555609
99.9%
Latin 7569
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1339025
24.1%
0 637773
11.5%
7 595399
10.7%
5 567327
10.2%
3 509489
 
9.2%
2 466916
 
8.4%
8 375565
 
6.8%
9 364501
 
6.6%
4 352742
 
6.3%
6 345250
 
6.2%
Latin
ValueCountFrequency (%)
M 7504
99.1%
N 42
 
0.6%
T 16
 
0.2%
S 2
 
< 0.1%
U 2
 
< 0.1%
P 2
 
< 0.1%
O 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5563178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1339025
24.1%
0 637773
11.5%
7 595399
10.7%
5 567327
10.2%
3 509489
 
9.2%
2 466916
 
8.4%
8 375565
 
6.8%
9 364501
 
6.6%
4 352742
 
6.3%
6 345250
 
6.2%
Other values (8) 9191
 
0.2%

Element Number Assigned
Categorical

HIGH CARDINALITY  MISSING 

Distinct4560
Distinct (%)0.5%
Missing59208
Missing (%)5.7%
Memory size7.9 MiB
EX01
 
12491
EX07
 
12222
OFFDTY
 
12204
EX10
 
7642
EX06
 
7500
Other values (4555)
927236 

Length

Max length6
Median length4
Mean length4.0322508
Min length1

Characters and Unicode

Total characters3948763
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique545 ?
Unique (%)0.1%

Sample

1st rowEX07
2nd rowB342
3rd rowB631
4th rowA354
5th rowA314

Common Values

ValueCountFrequency (%)
EX01 12491
 
1.2%
EX07 12222
 
1.2%
OFFDTY 12204
 
1.2%
EX10 7642
 
0.7%
EX06 7500
 
0.7%
EX04 7082
 
0.7%
C209 5006
 
0.5%
C509 4212
 
0.4%
EX08 4130
 
0.4%
EX09 3850
 
0.4%
Other values (4550) 902956
86.9%
(Missing) 59208
 
5.7%

Length

2023-04-21T15:41:33.394770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ex01 12491
 
1.3%
ex07 12222
 
1.2%
offdty 12204
 
1.2%
ex10 7642
 
0.8%
ex06 7500
 
0.8%
ex04 7082
 
0.7%
c209 5006
 
0.5%
c509 4212
 
0.4%
ex08 4130
 
0.4%
ex09 3850
 
0.4%
Other values (4539) 905597
92.2%

Most occurring characters

ValueCountFrequency (%)
2 456091
11.6%
1 453805
11.5%
3 442456
11.2%
4 410888
10.4%
5 373918
9.5%
B 229232
 
5.8%
6 222368
 
5.6%
7 210169
 
5.3%
C 200381
 
5.1%
E 157883
 
4.0%
Other values (24) 791572
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2832663
71.7%
Uppercase Letter 1113459
 
28.2%
Space Separator 2641
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 229232
20.6%
C 200381
18.0%
E 157883
14.2%
A 141737
12.7%
D 112402
10.1%
F 86246
 
7.7%
X 63205
 
5.7%
L 27128
 
2.4%
T 21971
 
2.0%
Y 15436
 
1.4%
Other values (13) 57838
 
5.2%
Decimal Number
ValueCountFrequency (%)
2 456091
16.1%
1 453805
16.0%
3 442456
15.6%
4 410888
14.5%
5 373918
13.2%
6 222368
7.9%
7 210169
7.4%
0 114461
 
4.0%
9 94484
 
3.3%
8 54023
 
1.9%
Space Separator
ValueCountFrequency (%)
2641
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2835304
71.8%
Latin 1113459
 
28.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 229232
20.6%
C 200381
18.0%
E 157883
14.2%
A 141737
12.7%
D 112402
10.1%
F 86246
 
7.7%
X 63205
 
5.7%
L 27128
 
2.4%
T 21971
 
2.0%
Y 15436
 
1.4%
Other values (13) 57838
 
5.2%
Common
ValueCountFrequency (%)
2 456091
16.1%
1 453805
16.0%
3 442456
15.6%
4 410888
14.5%
5 373918
13.2%
6 222368
7.8%
7 210169
7.4%
0 114461
 
4.0%
9 94484
 
3.3%
8 54023
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3948763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 456091
11.6%
1 453805
11.5%
3 442456
11.2%
4 410888
10.4%
5 373918
9.5%
B 229232
 
5.8%
6 222368
 
5.6%
7 210169
 
5.3%
C 200381
 
5.1%
E 157883
 
4.0%
Other values (24) 791572
20.0%

Investigating Unit 1
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing267376
Missing (%)25.7%
Memory size7.9 MiB
Investigations
705635 
Strategic Deployment
 
52704
Support
 
9024
Patrol
 
3762
Support Personnel
 
2

Length

Max length20
Median length14
Mean length14.289143
Min length6

Characters and Unicode

Total characters11018744
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInvestigations
2nd rowInvestigations
3rd rowInvestigations
4th rowInvestigations
5th rowInvestigations

Common Values

ValueCountFrequency (%)
Investigations 705635
67.9%
Strategic Deployment 52704
 
5.1%
Support 9024
 
0.9%
Patrol 3762
 
0.4%
Support Personnel 2
 
< 0.1%
(Missing) 267376
 
25.7%

Length

2023-04-21T15:41:33.461221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:33.537052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
investigations 705635
85.7%
strategic 52704
 
6.4%
deployment 52704
 
6.4%
support 9026
 
1.1%
patrol 3762
 
0.5%
personnel 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 1582170
14.4%
n 1463978
13.3%
i 1463974
13.3%
s 1411272
12.8%
e 863751
7.8%
o 771129
7.0%
a 762101
6.9%
g 758339
6.9%
I 705635
6.4%
v 705635
6.4%
Other values (11) 530760
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10142205
92.0%
Uppercase Letter 823833
 
7.5%
Space Separator 52706
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1582170
15.6%
n 1463978
14.4%
i 1463974
14.4%
s 1411272
13.9%
e 863751
8.5%
o 771129
7.6%
a 762101
7.5%
g 758339
7.5%
v 705635
7.0%
p 70756
 
0.7%
Other values (6) 289100
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
I 705635
85.7%
S 61730
 
7.5%
D 52704
 
6.4%
P 3764
 
0.5%
Space Separator
ValueCountFrequency (%)
52706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10966038
99.5%
Common 52706
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1582170
14.4%
n 1463978
13.4%
i 1463974
13.4%
s 1411272
12.9%
e 863751
7.9%
o 771129
7.0%
a 762101
6.9%
g 758339
6.9%
I 705635
6.4%
v 705635
6.4%
Other values (10) 478054
 
4.4%
Common
ValueCountFrequency (%)
52706
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11018744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1582170
14.4%
n 1463978
13.3%
i 1463974
13.3%
s 1411272
12.8%
e 863751
7.8%
o 771129
7.0%
a 762101
6.9%
g 758339
6.9%
I 705635
6.4%
v 705635
6.4%
Other values (11) 530760
 
4.8%

Investigating Unit 2
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)< 0.1%
Missing267357
Missing (%)25.7%
Memory size7.9 MiB
Special Investigations / Auto Theft
209052 
Property Crime Division / NE Property Crimes
71797 
Property Crime Division / NW Property Crimes
64843 
Property Crime Division / SW Property Crimes
57414 
Property Crime Division / NC Property Crimes
53445 
Other values (53)
314595 

Length

Max length44
Median length41
Mean length36.86131
Min length9

Characters and Unicode

Total characters28425452
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowProperty Crime Division / NE Property Crimes
2nd rowProperty Crime Division / SE Property Crimes
3rd rowProperty Crime Division / NC Property Crimes
4th rowCapers / Assaults
5th rowSpecial Investigations / Auto Theft

Common Values

ValueCountFrequency (%)
Special Investigations / Auto Theft 209052
20.1%
Property Crime Division / NE Property Crimes 71797
 
6.9%
Property Crime Division / NW Property Crimes 64843
 
6.2%
Property Crime Division / SW Property Crimes 57414
 
5.5%
Property Crime Division / NC Property Crimes 53445
 
5.1%
Capers / Assaults 49989
 
4.8%
Field Services / Vehicle Crimes Unit 48835
 
4.7%
Property Crime Division / SC Property Crimes 47436
 
4.6%
Property Crime Division / CE Property Crimes 36778
 
3.5%
Capers / Robbery 34944
 
3.4%
Other values (48) 96613
 
9.3%
(Missing) 267357
25.7%

Length

2023-04-21T15:41:33.716048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
771132
17.5%
property 728850
16.5%
crimes 434616
9.8%
division 372730
8.4%
crime 361332
8.2%
special 235809
 
5.3%
investigations 235311
 
5.3%
auto 209053
 
4.7%
theft 209053
 
4.7%
capers 106107
 
2.4%
Other values (72) 753023
17.0%

Most occurring characters

ValueCountFrequency (%)
3645870
12.8%
i 2915592
 
10.3%
e 2630871
 
9.3%
r 2476129
 
8.7%
t 1762750
 
6.2%
o 1643259
 
5.8%
s 1594849
 
5.6%
p 1092937
 
3.8%
C 1047323
 
3.7%
n 962909
 
3.4%
Other values (40) 8652963
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20000533
70.4%
Uppercase Letter 4007911
 
14.1%
Space Separator 3645870
 
12.8%
Other Punctuation 771132
 
2.7%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2915592
14.6%
e 2630871
13.2%
r 2476129
12.4%
t 1762750
8.8%
o 1643259
8.2%
s 1594849
8.0%
p 1092937
 
5.5%
n 962909
 
4.8%
m 824010
 
4.1%
y 771547
 
3.9%
Other values (13) 3325680
16.6%
Uppercase Letter
ValueCountFrequency (%)
C 1047323
26.1%
P 738417
18.4%
S 437105
10.9%
D 373059
 
9.3%
A 261123
 
6.5%
I 242610
 
6.1%
T 209330
 
5.2%
N 192484
 
4.8%
E 138082
 
3.4%
W 123623
 
3.1%
Other values (12) 244755
 
6.1%
Decimal Number
ValueCountFrequency (%)
0 2
33.3%
6 2
33.3%
9 2
33.3%
Space Separator
ValueCountFrequency (%)
3645870
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 771132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24008444
84.5%
Common 4417008
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2915592
12.1%
e 2630871
 
11.0%
r 2476129
 
10.3%
t 1762750
 
7.3%
o 1643259
 
6.8%
s 1594849
 
6.6%
p 1092937
 
4.6%
C 1047323
 
4.4%
n 962909
 
4.0%
m 824010
 
3.4%
Other values (35) 7057815
29.4%
Common
ValueCountFrequency (%)
3645870
82.5%
/ 771132
 
17.5%
0 2
 
< 0.1%
6 2
 
< 0.1%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28425452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3645870
12.8%
i 2915592
 
10.3%
e 2630871
 
9.3%
r 2476129
 
8.7%
t 1762750
 
6.2%
o 1643259
 
5.8%
s 1594849
 
5.6%
p 1092937
 
3.8%
C 1047323
 
3.7%
n 962909
 
3.4%
Other values (40) 8652963
30.4%

Offense Status
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing11338
Missing (%)1.1%
Memory size7.9 MiB
Suspended
858060 
Clear by Arrest
107173 
Clear by Exceptional Arrest
 
24369
Closed/Cleared
 
19642
Open
 
17888
Other values (2)
 
33

Length

Max length27
Median length9
Mean length10.06204
Min length1

Characters and Unicode

Total characters10335375
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSuspended
2nd rowOpen
3rd rowSuspended
4th rowClear by Arrest
5th rowSuspended

Common Values

ValueCountFrequency (%)
Suspended 858060
82.6%
Clear by Arrest 107173
 
10.3%
Clear by Exceptional Arrest 24369
 
2.3%
Closed/Cleared 19642
 
1.9%
Open 17888
 
1.7%
Returned for Correction 32
 
< 0.1%
L 1
 
< 0.1%
(Missing) 11338
 
1.1%

Length

2023-04-21T15:41:33.787322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:33.857101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
suspended 858060
65.3%
clear 131542
 
10.0%
by 131542
 
10.0%
arrest 131542
 
10.0%
exceptional 24369
 
1.9%
closed/cleared 19642
 
1.5%
open 17888
 
1.4%
returned 32
 
< 0.1%
for 32
 
< 0.1%
correction 32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 2080483
20.1%
d 1755436
17.0%
s 1009244
9.8%
n 900381
8.7%
p 900317
8.7%
u 858092
8.3%
S 858060
8.3%
r 414396
 
4.0%
287517
 
2.8%
l 195195
 
1.9%
Other values (16) 1076254
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8825466
85.4%
Uppercase Letter 1202750
 
11.6%
Space Separator 287517
 
2.8%
Other Punctuation 19642
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2080483
23.6%
d 1755436
19.9%
s 1009244
11.4%
n 900381
10.2%
p 900317
10.2%
u 858092
9.7%
r 414396
 
4.7%
l 195195
 
2.2%
a 175553
 
2.0%
t 155975
 
1.8%
Other values (7) 380394
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
S 858060
71.3%
C 170858
 
14.2%
A 131542
 
10.9%
E 24369
 
2.0%
O 17888
 
1.5%
R 32
 
< 0.1%
L 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
287517
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 19642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10028216
97.0%
Common 307159
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2080483
20.7%
d 1755436
17.5%
s 1009244
10.1%
n 900381
9.0%
p 900317
9.0%
u 858092
8.6%
S 858060
8.6%
r 414396
 
4.1%
l 195195
 
1.9%
a 175553
 
1.8%
Other values (14) 881059
8.8%
Common
ValueCountFrequency (%)
287517
93.6%
/ 19642
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10335375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2080483
20.1%
d 1755436
17.0%
s 1009244
9.8%
n 900381
8.7%
p 900317
8.7%
u 858092
8.3%
S 858060
8.3%
r 414396
 
4.0%
287517
 
2.8%
l 195195
 
1.9%
Other values (16) 1076254
10.4%

UCR Disposition
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing11188
Missing (%)1.1%
Memory size7.9 MiB
Suspended
858142 
CBA (Over Age 17)
101765 
CBEA (Over Age 17)
 
24122
Closed
 
19635
Open
 
17899
Other values (5)
 
5752

Length

Max length19
Median length9
Mean length9.8786769
Min length4

Characters and Unicode

Total characters10148513
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuspended
2nd rowOpen
3rd rowSuspended
4th rowCBA (Over Age 17)
5th rowSuspended

Common Values

ValueCountFrequency (%)
Suspended 858142
82.6%
CBA (Over Age 17) 101765
 
9.8%
CBEA (Over Age 17) 24122
 
2.3%
Closed 19635
 
1.9%
Open 17899
 
1.7%
CBA (Age 17) 4751
 
0.5%
CBEA (Age 17) 550
 
0.1%
CBEA (Under Age 17) 243
 
< 0.1%
CBA (Under 17) 199
 
< 0.1%
Pending 9
 
< 0.1%
(Missing) 11188
 
1.1%

Length

2023-04-21T15:41:33.927840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:34.007820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
suspended 858142
60.6%
17 131630
 
9.3%
age 131431
 
9.3%
over 125887
 
8.9%
cba 106715
 
7.5%
cbea 24915
 
1.8%
closed 19635
 
1.4%
open 17899
 
1.3%
under 442
 
< 0.1%
pending 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 2011587
19.8%
d 1736370
17.1%
s 877777
8.6%
n 876501
8.6%
p 876041
8.6%
S 858142
8.5%
u 858142
8.5%
389390
 
3.8%
A 263061
 
2.6%
C 151265
 
1.5%
Other values (15) 1250237
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7659353
75.5%
Uppercase Letter 1573250
 
15.5%
Space Separator 389390
 
3.8%
Decimal Number 263260
 
2.6%
Open Punctuation 131630
 
1.3%
Close Punctuation 131630
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2011587
26.3%
d 1736370
22.7%
s 877777
11.5%
n 876501
11.4%
p 876041
11.4%
u 858142
11.2%
g 131440
 
1.7%
r 126329
 
1.6%
v 125887
 
1.6%
l 19635
 
0.3%
Other values (2) 19644
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 858142
54.5%
A 263061
 
16.7%
C 151265
 
9.6%
O 143786
 
9.1%
B 131630
 
8.4%
E 24915
 
1.6%
U 442
 
< 0.1%
P 9
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 131630
50.0%
7 131630
50.0%
Space Separator
ValueCountFrequency (%)
389390
100.0%
Open Punctuation
ValueCountFrequency (%)
( 131630
100.0%
Close Punctuation
ValueCountFrequency (%)
) 131630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9232603
91.0%
Common 915910
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2011587
21.8%
d 1736370
18.8%
s 877777
9.5%
n 876501
9.5%
p 876041
9.5%
S 858142
9.3%
u 858142
9.3%
A 263061
 
2.8%
C 151265
 
1.6%
O 143786
 
1.6%
Other values (10) 579931
 
6.3%
Common
ValueCountFrequency (%)
389390
42.5%
( 131630
 
14.4%
1 131630
 
14.4%
7 131630
 
14.4%
) 131630
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10148513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2011587
19.8%
d 1736370
17.1%
s 877777
8.6%
n 876501
8.6%
p 876041
8.6%
S 858142
8.5%
u 858142
8.5%
389390
 
3.8%
A 263061
 
2.6%
C 151265
 
1.5%
Other values (15) 1250237
12.3%

Modus Operandi (MO)
Categorical

HIGH CARDINALITY  MISSING 

Distinct510789
Distinct (%)53.4%
Missing81448
Missing (%)7.8%
Memory size7.9 MiB
FOUND PROPERTY
 
16099
CRIMINAL TRESPASS WARNING
 
8498
PUBLIC INTOXICATION
 
4755
UNEXPLAINED DEATH
 
4219
ABANDONED PROPERTY
 
4090
Other values (510784)
919394 

Length

Max length399
Median length356
Mean length43.410896
Min length1

Characters and Unicode

Total characters41546615
Distinct characters80
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique421569 ?
Unique (%)44.0%

Sample

1st rowUNKN SUSP REMOVED THE REAR LICENSE PLATE OFF A NISSAN
2nd rowSUSP MADE THREATENING PHONE CALL TO COMP
3rd rowSUSP PRIED FRONT DOOR, DAMAGE ALARM PANEL, TAMPERED WITH SAFE
4th rowPUBLIC INTOXICATION
5th rowWITNESS OBSERVED THE SUSPECT FIRE A GUN IN THE AIR

Common Values

ValueCountFrequency (%)
FOUND PROPERTY 16099
 
1.6%
CRIMINAL TRESPASS WARNING 8498
 
0.8%
PUBLIC INTOXICATION 4755
 
0.5%
UNEXPLAINED DEATH 4219
 
0.4%
ABANDONED PROPERTY 4090
 
0.4%
INJURED PERSON 3918
 
0.4%
UUMV 3880
 
0.4%
ABANDONED VEHICLE 3584
 
0.3%
CT WARNING 3305
 
0.3%
LOST PROPERTY 2893
 
0.3%
Other values (510779) 901814
86.8%
(Missing) 81448
 
7.8%

Length

2023-04-21T15:41:34.131482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
susp 431178
 
6.3%
took 309466
 
4.5%
unk 293305
 
4.3%
vehicle 219241
 
3.2%
property 200398
 
2.9%
and 189067
 
2.7%
consent 184144
 
2.7%
suspect 164327
 
2.4%
comp's 159581
 
2.3%
comp 154478
 
2.2%
Other values (37448) 4573196
66.5%

Most occurring characters

ValueCountFrequency (%)
5940036
14.3%
O 3523206
 
8.5%
E 3478078
 
8.4%
S 3077578
 
7.4%
N 2827765
 
6.8%
T 2697410
 
6.5%
P 2223598
 
5.4%
I 1806499
 
4.3%
R 1773170
 
4.3%
U 1757516
 
4.2%
Other values (70) 12441759
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34741992
83.6%
Space Separator 5940036
 
14.3%
Other Punctuation 730115
 
1.8%
Decimal Number 79378
 
0.2%
Open Punctuation 21246
 
0.1%
Close Punctuation 21190
 
0.1%
Dash Punctuation 8500
 
< 0.1%
Currency Symbol 2741
 
< 0.1%
Math Symbol 1179
 
< 0.1%
Lowercase Letter 80
 
< 0.1%
Other values (3) 158
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 3523206
 
10.1%
E 3478078
 
10.0%
S 3077578
 
8.9%
N 2827765
 
8.1%
T 2697410
 
7.8%
P 2223598
 
6.4%
I 1806499
 
5.2%
R 1773170
 
5.1%
U 1757516
 
5.1%
C 1732045
 
5.0%
Other values (16) 9845127
28.3%
Other Punctuation
ValueCountFrequency (%)
. 251144
34.4%
/ 196091
26.9%
' 188135
25.8%
, 72926
 
10.0%
& 14060
 
1.9%
# 2185
 
0.3%
* 1857
 
0.3%
" 1209
 
0.2%
; 1110
 
0.2%
: 900
 
0.1%
Other values (5) 498
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 20
25.0%
n 12
15.0%
t 8
 
10.0%
r 8
 
10.0%
v 4
 
5.0%
d 4
 
5.0%
c 4
 
5.0%
o 4
 
5.0%
l 4
 
5.0%
g 4
 
5.0%
Other values (2) 8
 
10.0%
Decimal Number
ValueCountFrequency (%)
0 17857
22.5%
2 17103
21.5%
1 13927
17.5%
4 6580
 
8.3%
3 6564
 
8.3%
5 4976
 
6.3%
9 3671
 
4.6%
7 3075
 
3.9%
6 2906
 
3.7%
8 2719
 
3.4%
Math Symbol
ValueCountFrequency (%)
< 788
66.8%
> 277
 
23.5%
+ 51
 
4.3%
= 50
 
4.2%
~ 13
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 21216
99.9%
[ 28
 
0.1%
{ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 21176
99.9%
] 12
 
0.1%
} 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5940036
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8500
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2741
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 62
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 52
100.0%
Other Symbol
ValueCountFrequency (%)
� 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34742072
83.6%
Common 6804543
 
16.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5940036
87.3%
. 251144
 
3.7%
/ 196091
 
2.9%
' 188135
 
2.8%
, 72926
 
1.1%
( 21216
 
0.3%
) 21176
 
0.3%
0 17857
 
0.3%
2 17103
 
0.3%
& 14060
 
0.2%
Other values (32) 64799
 
1.0%
Latin
ValueCountFrequency (%)
O 3523206
 
10.1%
E 3478078
 
10.0%
S 3077578
 
8.9%
N 2827765
 
8.1%
T 2697410
 
7.8%
P 2223598
 
6.4%
I 1806499
 
5.2%
R 1773170
 
5.1%
U 1757516
 
5.1%
C 1732045
 
5.0%
Other values (28) 9845207
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41546571
> 99.9%
Specials 44
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5940036
14.3%
O 3523206
 
8.5%
E 3478078
 
8.4%
S 3077578
 
7.4%
N 2827765
 
6.8%
T 2697410
 
6.5%
P 2223598
 
5.4%
I 1806499
 
4.3%
R 1773170
 
4.3%
U 1757516
 
4.2%
Other values (69) 12441715
29.9%
Specials
ValueCountFrequency (%)
� 44
100.0%

Family Offense
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing57458
Missing (%)5.5%
Memory size7.9 MiB
False
981045 
(Missing)
 
57458
ValueCountFrequency (%)
False 981045
94.5%
(Missing) 57458
 
5.5%
2023-04-21T15:41:34.206285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Hate Crime
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing1037343
Missing (%)99.9%
Memory size2.0 MiB
False
 
686
True
 
474
(Missing)
1037343 
ValueCountFrequency (%)
False 686
 
0.1%
True 474
 
< 0.1%
(Missing) 1037343
99.9%
2023-04-21T15:41:34.253552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct24
Distinct (%)< 0.1%
Missing1319
Missing (%)0.1%
Memory size7.9 MiB
None
1012798 
Unknown
 
23928
Anti White
 
92
Anti Black Or African American
 
74
Anti Homosexual (Gays and Lesbians)
 
69
Other values (19)
 
223

Length

Max length39
Median length4
Mean length4.0774781
Min length4

Characters and Unicode

Total characters4229095
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 1012798
97.5%
Unknown 23928
 
2.3%
Anti White 92
 
< 0.1%
Anti Black Or African American 74
 
< 0.1%
Anti Homosexual (Gays and Lesbians) 69
 
< 0.1%
Anti Hispanic 33
 
< 0.1%
Anti Male Homosexual (Gay) 31
 
< 0.1%
Anti Other Ethnicity/Natl Origin 23
 
< 0.1%
Anti Jewish 22
 
< 0.1%
Anti Asian/Pacific Islander 20
 
< 0.1%
Other values (14) 94
 
< 0.1%
(Missing) 1319
 
0.1%

Length

2023-04-21T15:41:34.312947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 1012798
97.5%
unknown 23928
 
2.3%
anti 454
 
< 0.1%
homosexual 115
 
< 0.1%
white 92
 
< 0.1%
or 78
 
< 0.1%
black 74
 
< 0.1%
african 74
 
< 0.1%
american 74
 
< 0.1%
and 69
 
< 0.1%
Other values (32) 541
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 1085520
25.7%
o 1037000
24.5%
e 1013348
24.0%
N 1012829
23.9%
k 24012
 
0.6%
w 23954
 
0.6%
U 23928
 
0.6%
i 1152
 
< 0.1%
1113
 
< 0.1%
a 870
 
< 0.1%
Other values (39) 5369
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3189350
75.4%
Uppercase Letter 1038303
 
24.6%
Space Separator 1113
 
< 0.1%
Open Punctuation 123
 
< 0.1%
Close Punctuation 123
 
< 0.1%
Other Punctuation 60
 
< 0.1%
Dash Punctuation 23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1085520
34.0%
o 1037000
32.5%
e 1013348
31.8%
k 24012
 
0.8%
w 23954
 
0.8%
i 1152
 
< 0.1%
a 870
 
< 0.1%
t 711
 
< 0.1%
s 485
 
< 0.1%
l 362
 
< 0.1%
Other values (13) 1936
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1012829
97.5%
U 23928
 
2.3%
A 654
 
0.1%
H 152
 
< 0.1%
O 138
 
< 0.1%
G 119
 
< 0.1%
W 92
 
< 0.1%
L 84
 
< 0.1%
B 74
 
< 0.1%
M 58
 
< 0.1%
Other values (10) 175
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 52
86.7%
. 8
 
13.3%
Space Separator
ValueCountFrequency (%)
1113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4227653
> 99.9%
Common 1442
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1085520
25.7%
o 1037000
24.5%
e 1013348
24.0%
N 1012829
24.0%
k 24012
 
0.6%
w 23954
 
0.6%
U 23928
 
0.6%
i 1152
 
< 0.1%
a 870
 
< 0.1%
t 711
 
< 0.1%
Other values (33) 4329
 
0.1%
Common
ValueCountFrequency (%)
1113
77.2%
( 123
 
8.5%
) 123
 
8.5%
/ 52
 
3.6%
- 23
 
1.6%
. 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4229095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1085520
25.7%
o 1037000
24.5%
e 1013348
24.0%
N 1012829
23.9%
k 24012
 
0.6%
w 23954
 
0.6%
U 23928
 
0.6%
i 1152
 
< 0.1%
1113
 
< 0.1%
a 870
 
< 0.1%
Other values (39) 5369
 
0.1%

Weapon Used
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct36
Distinct (%)< 0.1%
Missing640948
Missing (%)61.7%
Memory size7.9 MiB
Other
234652 
None (Mutually Exclusive)
51653 
Handgun
42585 
Personal Weapons (Hands-Feet ETC)
32120 
Firearm (Type Not Stated)
 
5151
Other values (31)
31394 

Length

Max length39
Median length5
Mean length10.921734
Min length3

Characters and Unicode

Total characters4341990
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHandgun
2nd rowHandgun
3rd rowHandgun
4th rowHandgun
5th rowPersonal Weapons (Hands-Feet ETC)

Common Values

ValueCountFrequency (%)
Other 234652
 
22.6%
None (Mutually Exclusive) 51653
 
5.0%
Handgun 42585
 
4.1%
Personal Weapons (Hands-Feet ETC) 32120
 
3.1%
Firearm (Type Not Stated) 5151
 
0.5%
Vehicle 4968
 
0.5%
Threats 4310
 
0.4%
Other Cutting Stabbing Inst. 3367
 
0.3%
Omission/Neglect 2703
 
0.3%
Pocket Knife 2701
 
0.3%
Other values (26) 13345
 
1.3%
(Missing) 640948
61.7%

Length

2023-04-21T15:41:34.388111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other 240318
38.0%
none 51653
 
8.2%
mutually 51653
 
8.2%
exclusive 51653
 
8.2%
handgun 42585
 
6.7%
etc 32171
 
5.1%
personal 32120
 
5.1%
weapons 32120
 
5.1%
hands-feet 32120
 
5.1%
firearm 6630
 
1.0%
Other values (47) 59210
 
9.4%

Most occurring characters

ValueCountFrequency (%)
e 524013
 
12.1%
t 368118
 
8.5%
r 294455
 
6.8%
n 259801
 
6.0%
h 252023
 
5.8%
O 243753
 
5.6%
234678
 
5.4%
a 214017
 
4.9%
u 205804
 
4.7%
l 202751
 
4.7%
Other values (42) 1542577
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3147848
72.5%
Uppercase Letter 737762
 
17.0%
Space Separator 234678
 
5.4%
Open Punctuation 88924
 
2.0%
Close Punctuation 88924
 
2.0%
Dash Punctuation 32120
 
0.7%
Other Punctuation 11734
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 524013
16.6%
t 368118
11.7%
r 294455
9.4%
n 259801
8.3%
h 252023
8.0%
a 214017
6.8%
u 205804
 
6.5%
l 202751
 
6.4%
s 167051
 
5.3%
o 135368
 
4.3%
Other values (14) 524447
16.7%
Uppercase Letter
ValueCountFrequency (%)
O 243753
33.0%
E 83973
 
11.4%
H 74705
 
10.1%
N 59532
 
8.1%
M 54881
 
7.4%
T 42624
 
5.8%
F 38821
 
5.3%
C 36447
 
4.9%
P 34919
 
4.7%
W 32613
 
4.4%
Other values (11) 35494
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/ 8316
70.9%
. 3367
28.7%
, 51
 
0.4%
Space Separator
ValueCountFrequency (%)
234678
100.0%
Open Punctuation
ValueCountFrequency (%)
( 88924
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88924
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3885610
89.5%
Common 456380
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 524013
13.5%
t 368118
 
9.5%
r 294455
 
7.6%
n 259801
 
6.7%
h 252023
 
6.5%
O 243753
 
6.3%
a 214017
 
5.5%
u 205804
 
5.3%
l 202751
 
5.2%
s 167051
 
4.3%
Other values (35) 1153824
29.7%
Common
ValueCountFrequency (%)
234678
51.4%
( 88924
 
19.5%
) 88924
 
19.5%
- 32120
 
7.0%
/ 8316
 
1.8%
. 3367
 
0.7%
, 51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4341990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 524013
 
12.1%
t 368118
 
8.5%
r 294455
 
6.8%
n 259801
 
6.0%
h 252023
 
5.8%
O 243753
 
5.6%
234678
 
5.4%
a 214017
 
4.9%
u 205804
 
4.7%
l 202751
 
4.7%
Other values (42) 1542577
35.5%

Gang Related Offense
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing579035
Missing (%)55.8%
Memory size7.9 MiB
No
354893 
UNK
101474 
G
 
1936
Yes
 
1098
J
 
66

Length

Max length3
Median length2
Mean length2.2188814
Min length1

Characters and Unicode

Total characters1019505
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUNK
2nd rowUNK
3rd rowUNK
4th rowUNK
5th rowG

Common Values

ValueCountFrequency (%)
No 354893
34.2%
UNK 101474
 
9.8%
G 1936
 
0.2%
Yes 1098
 
0.1%
J 66
 
< 0.1%
0 1
 
< 0.1%
(Missing) 579035
55.8%

Length

2023-04-21T15:41:34.458983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:34.533267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 354893
77.2%
unk 101474
 
22.1%
g 1936
 
0.4%
yes 1098
 
0.2%
j 66
 
< 0.1%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 456367
44.8%
o 354893
34.8%
U 101474
 
10.0%
K 101474
 
10.0%
G 1936
 
0.2%
Y 1098
 
0.1%
e 1098
 
0.1%
s 1098
 
0.1%
J 66
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 662415
65.0%
Lowercase Letter 357089
35.0%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 456367
68.9%
U 101474
 
15.3%
K 101474
 
15.3%
G 1936
 
0.3%
Y 1098
 
0.2%
J 66
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 354893
99.4%
e 1098
 
0.3%
s 1098
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1019504
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 456367
44.8%
o 354893
34.8%
U 101474
 
10.0%
K 101474
 
10.0%
G 1936
 
0.2%
Y 1098
 
0.1%
e 1098
 
0.1%
s 1098
 
0.1%
J 66
 
< 0.1%
Common
ValueCountFrequency (%)
0 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1019505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 456367
44.8%
o 354893
34.8%
U 101474
 
10.0%
K 101474
 
10.0%
G 1936
 
0.2%
Y 1098
 
0.1%
e 1098
 
0.1%
s 1098
 
0.1%
J 66
 
< 0.1%
0 1
 
< 0.1%

Drug Related Istevencident
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing57549
Missing (%)5.5%
Memory size7.9 MiB
No
856226 
UNK
93330 
Yes
 
31364
Unknown
 
30
2
 
2

Length

Max length7
Median length2
Mean length2.1272639
Min length1

Characters and Unicode

Total characters2086748
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 856226
82.4%
UNK 93330
 
9.0%
Yes 31364
 
3.0%
Unknown 30
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
(Missing) 57549
 
5.5%

Length

2023-04-21T15:41:34.591493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:34.658657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 856226
87.3%
unk 93330
 
9.5%
yes 31364
 
3.2%
unknown 30
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 949556
45.5%
o 856256
41.0%
U 93360
 
4.5%
K 93330
 
4.5%
Y 31364
 
1.5%
e 31364
 
1.5%
s 31364
 
1.5%
n 90
 
< 0.1%
k 30
 
< 0.1%
w 30
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1167610
56.0%
Lowercase Letter 919134
44.0%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 856256
93.2%
e 31364
 
3.4%
s 31364
 
3.4%
n 90
 
< 0.1%
k 30
 
< 0.1%
w 30
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 949556
81.3%
U 93360
 
8.0%
K 93330
 
8.0%
Y 31364
 
2.7%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2086744
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 949556
45.5%
o 856256
41.0%
U 93360
 
4.5%
K 93330
 
4.5%
Y 31364
 
1.5%
e 31364
 
1.5%
s 31364
 
1.5%
n 90
 
< 0.1%
k 30
 
< 0.1%
w 30
 
< 0.1%
Common
ValueCountFrequency (%)
2 2
50.0%
3 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2086748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 949556
45.5%
o 856256
41.0%
U 93360
 
4.5%
K 93330
 
4.5%
Y 31364
 
1.5%
e 31364
 
1.5%
s 31364
 
1.5%
n 90
 
< 0.1%
k 30
 
< 0.1%
w 30
 
< 0.1%
Other values (2) 4
 
< 0.1%

RMS Code
Categorical

Distinct1291
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
MA-22990004-F1
101154 
FS-24110003-G13
 
59536
FS-24110003-G1
 
37180
FS-24110003-G14
 
36324
NA-99999999-X3
 
32506
Other values (1286)
771803 

Length

Max length17
Median length16
Mean length14.868286
Min length14

Characters and Unicode

Total characters15440760
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)< 0.1%

Sample

1st rowMC-99999999-F133
2nd rowMB-13160012-T35
3rd rowFS-22990001-E1
4th rowMC-99999999-NC313
5th rowF3-52130005-D48

Common Values

ValueCountFrequency (%)
MA-22990004-F1 101154
 
9.7%
FS-24110003-G13 59536
 
5.7%
FS-24110003-G1 37180
 
3.6%
FS-24110003-G14 36324
 
3.5%
NA-99999999-X3 32506
 
3.1%
NA-99999999-MSC11 29770
 
2.9%
F2-22990002-E5 28339
 
2.7%
FS-22990001-E1 27447
 
2.6%
MB-29990042-L99 27443
 
2.6%
NA-99999999-X6 25671
 
2.5%
Other values (1281) 633133
61.0%

Length

2023-04-21T15:41:34.719139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ma-22990004-f1 101154
 
9.7%
fs-24110003-g13 59536
 
5.7%
fs-24110003-g1 37180
 
3.6%
fs-24110003-g14 36324
 
3.5%
na-99999999-x3 32506
 
3.1%
na-99999999-msc11 29770
 
2.9%
f2-22990002-e5 28339
 
2.7%
fs-22990001-e1 27447
 
2.6%
mb-29990042-l99 27443
 
2.6%
na-99999999-x6 25671
 
2.5%
Other values (1281) 633133
61.0%

Most occurring characters

ValueCountFrequency (%)
9 3648939
23.6%
- 2077006
13.5%
0 2066031
13.4%
1 1374094
 
8.9%
2 1162711
 
7.5%
3 646224
 
4.2%
4 604784
 
3.9%
F 590911
 
3.8%
M 537473
 
3.5%
A 418136
 
2.7%
Other values (27) 2314451
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10217530
66.2%
Uppercase Letter 3141073
 
20.3%
Dash Punctuation 2077006
 
13.5%
Other Punctuation 5151
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 590911
18.8%
M 537473
17.1%
A 418136
13.3%
S 294133
9.4%
N 240539
7.7%
C 222047
 
7.1%
B 163058
 
5.2%
G 161868
 
5.2%
L 108908
 
3.5%
X 98088
 
3.1%
Other values (15) 305912
9.7%
Decimal Number
ValueCountFrequency (%)
9 3648939
35.7%
0 2066031
20.2%
1 1374094
 
13.4%
2 1162711
 
11.4%
3 646224
 
6.3%
4 604784
 
5.9%
5 280028
 
2.7%
6 168401
 
1.6%
7 134030
 
1.3%
8 132288
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 2077006
100.0%
Other Punctuation
ValueCountFrequency (%)
* 5151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12299687
79.7%
Latin 3141073
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 590911
18.8%
M 537473
17.1%
A 418136
13.3%
S 294133
9.4%
N 240539
7.7%
C 222047
 
7.1%
B 163058
 
5.2%
G 161868
 
5.2%
L 108908
 
3.5%
X 98088
 
3.1%
Other values (15) 305912
9.7%
Common
ValueCountFrequency (%)
9 3648939
29.7%
- 2077006
16.9%
0 2066031
16.8%
1 1374094
 
11.2%
2 1162711
 
9.5%
3 646224
 
5.3%
4 604784
 
4.9%
5 280028
 
2.3%
6 168401
 
1.4%
7 134030
 
1.1%
Other values (2) 137439
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15440760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 3648939
23.6%
- 2077006
13.5%
0 2066031
13.4%
1 1374094
 
8.9%
2 1162711
 
7.5%
3 646224
 
4.2%
4 604784
 
3.9%
F 590911
 
3.8%
M 537473
 
3.5%
A 418136
 
2.7%
Other values (27) 2314451
15.0%
Distinct661
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48140583
Minimum7391108
Maximum99999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:34.791921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7391108
5-th percentile13160012
Q122990004
median24110003
Q399999999
95-th percentile99999999
Maximum99999999
Range92608891
Interquartile range (IQR)77009995

Descriptive statistics

Standard deviation34313664
Coefficient of variation (CV)0.71278039
Kurtosis-1.2819137
Mean48140583
Median Absolute Deviation (MAD)5880039
Skewness0.74401822
Sum4.999414 × 1013
Variance1.1774275 × 1015
MonotonicityNot monotonic
2023-04-21T15:41:34.869148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99999999 300905
29.0%
24110003 148915
14.3%
22990004 107125
 
10.3%
22990002 44848
 
4.3%
22990001 34026
 
3.3%
29990042 27443
 
2.6%
29990016 24491
 
2.4%
12990002 24177
 
2.3%
23990191 22714
 
2.2%
23990067 17649
 
1.7%
Other values (651) 286210
27.6%
ValueCountFrequency (%)
7391108 5
 
< 0.1%
9990017 41
 
< 0.1%
9990018 119
 
< 0.1%
9990019 246
< 0.1%
9990022 130
 
< 0.1%
9990023 1
 
< 0.1%
9990026 33
 
< 0.1%
9990030 378
< 0.1%
10990001 47
 
< 0.1%
10990003 1
 
< 0.1%
ValueCountFrequency (%)
99999999 300905
29.0%
73991084 150
 
< 0.1%
73991080 1
 
< 0.1%
73991077 1
 
< 0.1%
73991067 1
 
< 0.1%
73991065 16
 
< 0.1%
73991064 32
 
< 0.1%
73991060 6
 
< 0.1%
73991058 5
 
< 0.1%
73991053 1
 
< 0.1%

Penal Code
Categorical

Distinct607
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
PC 31.07
161868 
No Offense
126062 
PC 30.04(a)
106651 
PC 28.03(b)(2)
 
51934
PC 30.02(c)(2)
 
44848
Other values (602)
547140 

Length

Max length28
Median length25
Mean length11.863718
Min length2

Characters and Unicode

Total characters12320507
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)< 0.1%

Sample

1st rowPC 31.03(f)
2nd rowPC 42.07(c)
3rd rowPC 30.02(c)(1)
4th rowPC 49.02
5th rowPC 22.05(b)

Common Values

ValueCountFrequency (%)
PC 31.07 161868
 
15.6%
No Offense 126062
 
12.1%
PC 30.04(a) 106651
 
10.3%
PC 28.03(b)(2) 51934
 
5.0%
PC 30.02(c)(2) 44848
 
4.3%
PC 30.02(c)(1) 34026
 
3.3%
PC 49.02 27667
 
2.7%
PC 31.03(e)(3) 26288
 
2.5%
No Violation 25671
 
2.5%
PC 29.03 24177
 
2.3%
Other values (597) 409311
39.4%

Length

2023-04-21T15:41:34.946402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pc 765327
37.0%
31.07 161868
 
7.8%
no 158769
 
7.7%
offense 133057
 
6.4%
30.04(a 106651
 
5.2%
28.03(b)(2 51934
 
2.5%
30.02(c)(2 44848
 
2.2%
30.02(c)(1 34026
 
1.6%
hsc 31248
 
1.5%
49.02 27667
 
1.3%
Other values (628) 555332
26.8%

Most occurring characters

ValueCountFrequency (%)
) 1036064
 
8.4%
( 1035481
 
8.4%
1032224
 
8.4%
0 1022909
 
8.3%
C 836891
 
6.8%
. 829376
 
6.7%
3 801489
 
6.5%
P 765637
 
6.2%
2 666212
 
5.4%
1 492877
 
4.0%
Other values (53) 3801347
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3807005
30.9%
Lowercase Letter 2344762
19.0%
Uppercase Letter 2225829
18.1%
Close Punctuation 1036064
 
8.4%
Open Punctuation 1035481
 
8.4%
Space Separator 1032224
 
8.4%
Other Punctuation 829515
 
6.7%
Dash Punctuation 9624
 
0.1%
Other Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 412690
17.6%
f 295089
12.6%
a 267929
11.4%
o 246422
10.5%
n 225094
9.6%
s 183150
7.8%
b 147725
 
6.3%
i 141731
 
6.0%
c 107142
 
4.6%
t 91447
 
3.9%
Other values (13) 226343
9.7%
Uppercase Letter
ValueCountFrequency (%)
C 836891
37.6%
P 765637
34.4%
N 162767
 
7.3%
O 138188
 
6.2%
A 97758
 
4.4%
S 32827
 
1.5%
H 31980
 
1.4%
I 29833
 
1.3%
V 27903
 
1.3%
T 22306
 
1.0%
Other values (12) 79739
 
3.6%
Decimal Number
ValueCountFrequency (%)
0 1022909
26.9%
3 801489
21.1%
2 666212
17.5%
1 492877
12.9%
4 297478
 
7.8%
7 184280
 
4.8%
8 149741
 
3.9%
5 107707
 
2.8%
9 74439
 
2.0%
6 9873
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 829376
> 99.9%
, 117
 
< 0.1%
/ 22
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1036064
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1035481
100.0%
Space Separator
ValueCountFrequency (%)
1032224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9624
100.0%
Other Symbol
ValueCountFrequency (%)
� 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7749916
62.9%
Latin 4570591
37.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 836891
18.3%
P 765637
16.8%
e 412690
9.0%
f 295089
 
6.5%
a 267929
 
5.9%
o 246422
 
5.4%
n 225094
 
4.9%
s 183150
 
4.0%
N 162767
 
3.6%
b 147725
 
3.2%
Other values (35) 1027197
22.5%
Common
ValueCountFrequency (%)
) 1036064
13.4%
( 1035481
13.4%
1032224
13.3%
0 1022909
13.2%
. 829376
10.7%
3 801489
10.3%
2 666212
8.6%
1 492877
6.4%
4 297478
 
3.8%
7 184280
 
2.4%
Other values (8) 351526
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12320504
> 99.9%
Specials 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 1036064
 
8.4%
( 1035481
 
8.4%
1032224
 
8.4%
0 1022909
 
8.3%
C 836891
 
6.8%
. 829376
 
6.7%
3 801489
 
6.5%
P 765637
 
6.2%
2 666212
 
5.4%
1 492877
 
4.0%
Other values (52) 3801344
30.9%
Specials
ValueCountFrequency (%)
� 3
100.0%

UCR Offense Name
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)< 0.1%
Missing631134
Missing (%)60.8%
Memory size7.9 MiB
THEFT/BMV
63339 
UUMV
58580 
VANDALISM & CRIM MISCHIEF
53763 
FOUND
37457 
OTHER THEFTS
30422 
Other values (46)
163808 

Length

Max length25
Median length21
Mean length12.576622
Min length3

Characters and Unicode

Total characters5123326
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFOUND
2nd rowFOUND
3rd rowFOUND
4th rowUUMV
5th rowDRUNK & DISORDERLY

Common Values

ValueCountFrequency (%)
THEFT/BMV 63339
 
6.1%
UUMV 58580
 
5.6%
VANDALISM & CRIM MISCHIEF 53763
 
5.2%
FOUND 37457
 
3.6%
OTHER THEFTS 30422
 
2.9%
BURGLARY-RESIDENCE 28179
 
2.7%
BURGLARY-BUSINESS 15131
 
1.5%
ROBBERY-INDIVIDUAL 12796
 
1.2%
ASSAULT 11819
 
1.1%
DRUNK & DISORDERLY 11661
 
1.1%
Other values (41) 84222
 
8.1%
(Missing) 631134
60.8%

Length

2023-04-21T15:41:35.017619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78865
 
11.2%
theft/bmv 63339
 
9.0%
uumv 58580
 
8.3%
vandalism 53763
 
7.7%
crim 53763
 
7.7%
mischief 53763
 
7.7%
found 37457
 
5.3%
other 30422
 
4.3%
thefts 30422
 
4.3%
burglary-residence 28179
 
4.0%
Other values (70) 214198
30.5%

Most occurring characters

ValueCountFrequency (%)
E 393703
 
7.7%
I 382823
 
7.5%
S 325559
 
6.4%
R 323183
 
6.3%
T 317653
 
6.2%
M 307166
 
6.0%
U 301049
 
5.9%
295382
 
5.8%
D 259030
 
5.1%
A 253737
 
5.0%
Other values (20) 1964041
38.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4613028
90.0%
Space Separator 295382
 
5.8%
Other Punctuation 148017
 
2.9%
Dash Punctuation 66899
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 393703
 
8.5%
I 382823
 
8.3%
S 325559
 
7.1%
R 323183
 
7.0%
T 317653
 
6.9%
M 307166
 
6.7%
U 301049
 
6.5%
D 259030
 
5.6%
A 253737
 
5.5%
F 229254
 
5.0%
Other values (16) 1519871
32.9%
Other Punctuation
ValueCountFrequency (%)
& 76642
51.8%
/ 71375
48.2%
Space Separator
ValueCountFrequency (%)
295382
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4613028
90.0%
Common 510298
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 393703
 
8.5%
I 382823
 
8.3%
S 325559
 
7.1%
R 323183
 
7.0%
T 317653
 
6.9%
M 307166
 
6.7%
U 301049
 
6.5%
D 259030
 
5.6%
A 253737
 
5.5%
F 229254
 
5.0%
Other values (16) 1519871
32.9%
Common
ValueCountFrequency (%)
295382
57.9%
& 76642
 
15.0%
/ 71375
 
14.0%
- 66899
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5123326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 393703
 
7.7%
I 382823
 
7.5%
S 325559
 
6.4%
R 323183
 
6.3%
T 317653
 
6.2%
M 307166
 
6.0%
U 301049
 
5.9%
295382
 
5.8%
D 259030
 
5.1%
A 253737
 
5.0%
Other values (20) 1964041
38.3%

UCR Offense Description
Categorical

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)< 0.1%
Missing631133
Missing (%)60.8%
Memory size7.9 MiB
THEFT
101797 
AUTO THEFT - UUMV
53447 
CRIMINAL MISCHIEF/VANDALISM
53135 
BURGLARY
43310 
FOUND PROPERTY
37457 
Other values (39)
118224 

Length

Max length28
Median length25
Mean length13.092596
Min length3

Characters and Unicode

Total characters5333531
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowFOUND PROPERTY
2nd rowFOUND PROPERTY
3rd rowFOUND PROPERTY
4th rowMOTOR VEHICLE THEFT
5th rowDRUNK & DISORDERLY

Common Values

ValueCountFrequency (%)
THEFT 101797
 
9.8%
AUTO THEFT - UUMV 53447
 
5.1%
CRIMINAL MISCHIEF/VANDALISM 53135
 
5.1%
BURGLARY 43310
 
4.2%
FOUND PROPERTY 37457
 
3.6%
ROBBERY 16024
 
1.5%
ASSAULT 12393
 
1.2%
MOTOR VEHICLE ACCIDENT 12194
 
1.2%
DRUNK & DISORDERLY 11661
 
1.1%
OTHER OFFENSES 9781
 
0.9%
Other values (34) 56171
 
5.4%
(Missing) 631133
60.8%

Length

2023-04-21T15:41:35.087681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
theft 160377
20.9%
68268
 
8.9%
uumv 53447
 
7.0%
auto 53447
 
7.0%
criminal 53135
 
6.9%
mischief/vandalism 53135
 
6.9%
burglary 43310
 
5.7%
property 41790
 
5.5%
found 37457
 
4.9%
assault 19910
 
2.6%
Other values (54) 181499
23.7%

Most occurring characters

ValueCountFrequency (%)
T 520713
 
9.8%
E 414270
 
7.8%
R 366611
 
6.9%
A 363355
 
6.8%
358405
 
6.7%
I 338870
 
6.4%
U 301128
 
5.6%
F 285659
 
5.4%
H 247794
 
4.6%
O 245185
 
4.6%
Other values (22) 1891541
35.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4850397
90.9%
Space Separator 358405
 
6.7%
Other Punctuation 70692
 
1.3%
Dash Punctuation 53447
 
1.0%
Open Punctuation 295
 
< 0.1%
Close Punctuation 295
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 520713
 
10.7%
E 414270
 
8.5%
R 366611
 
7.6%
A 363355
 
7.5%
I 338870
 
7.0%
U 301128
 
6.2%
F 285659
 
5.9%
H 247794
 
5.1%
O 245185
 
5.1%
M 238100
 
4.9%
Other values (16) 1528712
31.5%
Other Punctuation
ValueCountFrequency (%)
/ 55871
79.0%
& 14821
 
21.0%
Space Separator
ValueCountFrequency (%)
358405
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53447
100.0%
Open Punctuation
ValueCountFrequency (%)
( 295
100.0%
Close Punctuation
ValueCountFrequency (%)
) 295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4850397
90.9%
Common 483134
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 520713
 
10.7%
E 414270
 
8.5%
R 366611
 
7.6%
A 363355
 
7.5%
I 338870
 
7.0%
U 301128
 
6.2%
F 285659
 
5.9%
H 247794
 
5.1%
O 245185
 
5.1%
M 238100
 
4.9%
Other values (16) 1528712
31.5%
Common
ValueCountFrequency (%)
358405
74.2%
/ 55871
 
11.6%
- 53447
 
11.1%
& 14821
 
3.1%
( 295
 
0.1%
) 295
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5333531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 520713
 
9.8%
E 414270
 
7.8%
R 366611
 
6.9%
A 363355
 
6.8%
358405
 
6.7%
I 338870
 
6.4%
U 301128
 
5.6%
F 285659
 
5.4%
H 247794
 
4.6%
O 245185
 
4.6%
Other values (22) 1891541
35.5%

UCR Code
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct65
Distinct (%)< 0.1%
Missing631133
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean1399.3705
Minimum110
Maximum5700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:35.158017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile400
Q1640
median710
Q31800
95-th percentile4300
Maximum5700
Range5590
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation1218.7555
Coefficient of variation (CV)0.87093124
Kurtosis0.57965346
Mean1399.3705
Median Absolute Deviation (MAD)199
Skewness1.3714233
Sum5.7006155 × 108
Variance1485364.9
MonotonicityNot monotonic
2023-04-21T15:41:35.228646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 53763
 
5.2%
640 48996
 
4.7%
710 43575
 
4.2%
4300 29909
 
2.9%
690 27460
 
2.6%
511 19035
 
1.8%
2600 17349
 
1.7%
300 16024
 
1.5%
650 14198
 
1.4%
3200 12795
 
1.2%
Other values (55) 124266
 
12.0%
(Missing) 631133
60.8%
ValueCountFrequency (%)
110 402
 
< 0.1%
120 1
 
< 0.1%
300 16024
1.5%
400 7559
 
0.7%
450 11867
1.1%
511 19035
1.8%
512 12306
1.2%
521 8512
0.8%
522 2643
 
0.3%
531 632
 
0.1%
ValueCountFrequency (%)
5700 28
 
< 0.1%
5600 472
 
< 0.1%
4400 1
 
< 0.1%
4300 29909
2.9%
4200 4329
 
0.4%
4000 5342
 
0.5%
3700 1277
 
0.1%
3600 353
 
< 0.1%
3500 182
 
< 0.1%
3400 1501
 
0.1%

Offense Type
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing631133
Missing (%)60.8%
Memory size7.9 MiB
PART1
219822 
PART2
124973 
NOT CODED
62575 

Length

Max length9
Median length5
Mean length5.6144291
Min length5

Characters and Unicode

Total characters2287150
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT CODED
2nd rowNOT CODED
3rd rowNOT CODED
4th rowPART1
5th rowPART2

Common Values

ValueCountFrequency (%)
PART1 219822
 
21.2%
PART2 124973
 
12.0%
NOT CODED 62575
 
6.0%
(Missing) 631133
60.8%

Length

2023-04-21T15:41:35.301149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:35.376785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
part1 219822
46.8%
part2 124973
26.6%
not 62575
 
13.3%
coded 62575
 
13.3%

Most occurring characters

ValueCountFrequency (%)
T 407370
17.8%
P 344795
15.1%
A 344795
15.1%
R 344795
15.1%
1 219822
9.6%
O 125150
 
5.5%
D 125150
 
5.5%
2 124973
 
5.5%
N 62575
 
2.7%
62575
 
2.7%
Other values (2) 125150
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1879780
82.2%
Decimal Number 344795
 
15.1%
Space Separator 62575
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 407370
21.7%
P 344795
18.3%
A 344795
18.3%
R 344795
18.3%
O 125150
 
6.7%
D 125150
 
6.7%
N 62575
 
3.3%
C 62575
 
3.3%
E 62575
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 219822
63.8%
2 124973
36.2%
Space Separator
ValueCountFrequency (%)
62575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1879780
82.2%
Common 407370
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 407370
21.7%
P 344795
18.3%
A 344795
18.3%
R 344795
18.3%
O 125150
 
6.7%
D 125150
 
6.7%
N 62575
 
3.3%
C 62575
 
3.3%
E 62575
 
3.3%
Common
ValueCountFrequency (%)
1 219822
54.0%
2 124973
30.7%
62575
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2287150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 407370
17.8%
P 344795
15.1%
A 344795
15.1%
R 344795
15.1%
1 219822
9.6%
O 125150
 
5.5%
D 125150
 
5.5%
2 124973
 
5.5%
N 62575
 
2.7%
62575
 
2.7%
Other values (2) 125150
 
5.5%

NIBRS Crime
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
MISCELLANEOUS
173234 
UUMV
125866 
THEFT FROM MOTOR VEHICLE
75500 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY
61068 
ALL OTHER LARCENY
45400 
Other values (49)
300406 

Length

Max length43
Median length41
Mean length18.189908
Min length3

Characters and Unicode

Total characters14214940
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTHEFT OF MOTOR VEHICLE PARTS OR ACCESSORIES
2nd rowINTIMIDATION
3rd rowBURGLARY-BUSINESS
4th rowPUBLIC INTOXICATION
5th rowWEAPON LAW VIOLATIONS

Common Values

ValueCountFrequency (%)
MISCELLANEOUS 173234
16.7%
UUMV 125866
12.1%
THEFT FROM MOTOR VEHICLE 75500
 
7.3%
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY 61068
 
5.9%
ALL OTHER LARCENY 45400
 
4.4%
THEFT OF MOTOR VEHICLE PARTS OR ACCESSORIES 27717
 
2.7%
BURGLARY-RESIDENCE 25177
 
2.4%
BURGLARY-BUSINESS 24993
 
2.4%
ALL OTHER OFFENSES 24127
 
2.3%
DRUG/ NARCOTIC VIOLATIONS 23875
 
2.3%
Other values (44) 174517
16.8%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.446796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miscellaneous 173234
 
9.4%
uumv 125866
 
6.8%
theft 108398
 
5.9%
motor 103217
 
5.6%
vehicle 103217
 
5.6%
of 98685
 
5.3%
from 75500
 
4.1%
all 69527
 
3.8%
other 69527
 
3.8%
property 68338
 
3.7%
Other values (96) 852235
46.1%

Most occurring characters

ValueCountFrequency (%)
E 1315762
 
9.3%
O 1108946
 
7.8%
1066270
 
7.5%
A 964960
 
6.8%
S 943795
 
6.6%
L 931371
 
6.6%
I 916751
 
6.4%
R 876388
 
6.2%
T 862928
 
6.1%
U 705220
 
5.0%
Other values (21) 4522549
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12871318
90.5%
Space Separator 1066270
 
7.5%
Other Punctuation 163975
 
1.2%
Dash Punctuation 113377
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1315762
 
10.2%
O 1108946
 
8.6%
A 964960
 
7.5%
S 943795
 
7.3%
L 931371
 
7.2%
I 916751
 
7.1%
R 876388
 
6.8%
T 862928
 
6.7%
U 705220
 
5.5%
N 682264
 
5.3%
Other values (16) 3562933
27.7%
Other Punctuation
ValueCountFrequency (%)
/ 161716
98.6%
, 1699
 
1.0%
& 560
 
0.3%
Space Separator
ValueCountFrequency (%)
1066270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 113377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12871318
90.5%
Common 1343622
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1315762
 
10.2%
O 1108946
 
8.6%
A 964960
 
7.5%
S 943795
 
7.3%
L 931371
 
7.2%
I 916751
 
7.1%
R 876388
 
6.8%
T 862928
 
6.7%
U 705220
 
5.5%
N 682264
 
5.3%
Other values (16) 3562933
27.7%
Common
ValueCountFrequency (%)
1066270
79.4%
/ 161716
 
12.0%
- 113377
 
8.4%
, 1699
 
0.1%
& 560
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14214940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1315762
 
9.3%
O 1108946
 
7.8%
1066270
 
7.5%
A 964960
 
6.8%
S 943795
 
6.6%
L 931371
 
6.6%
I 916751
 
6.4%
R 876388
 
6.2%
T 862928
 
6.1%
U 705220
 
5.0%
Other values (21) 4522549
31.8%

NIBRS Crime Category
Categorical

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
MISCELLANEOUS
173234 
LARCENY/ THEFT OFFENSES
165666 
MOTOR VEHICLE THEFT
125866 
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY
61068 
BURGLARY/ BREAKING & ENTERING
50170 
Other values (27)
205470 

Length

Max length42
Median length29
Mean length20.944948
Min length5

Characters and Unicode

Total characters16367932
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLARCENY/ THEFT OFFENSES
2nd rowASSAULT OFFENSES
3rd rowBURGLARY/ BREAKING & ENTERING
4th rowPUBLIC INTOXICATION
5th rowWEAPON LAW VIOLATIONS

Common Values

ValueCountFrequency (%)
MISCELLANEOUS 173234
16.7%
LARCENY/ THEFT OFFENSES 165666
16.0%
MOTOR VEHICLE THEFT 125866
12.1%
DESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTY 61068
 
5.9%
BURGLARY/ BREAKING & ENTERING 50170
 
4.8%
ASSAULT OFFENSES 49182
 
4.7%
DRUG/ NARCOTIC VIOLATIONS 25487
 
2.5%
ALL OTHER OFFENSES 24127
 
2.3%
ROBBERY 22078
 
2.1%
PUBLIC INTOXICATION 21214
 
2.0%
Other values (22) 63382
 
6.1%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.528440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
theft 291532
14.1%
offenses 252361
 
12.2%
miscellaneous 173234
 
8.4%
larceny 165666
 
8.0%
motor 125866
 
6.1%
vehicle 125866
 
6.1%
73943
 
3.6%
property 68338
 
3.3%
of 67490
 
3.3%
destruction 61068
 
2.9%
Other values (52) 666641
32.2%

Most occurring characters

ValueCountFrequency (%)
E 2022326
12.4%
1290531
 
7.9%
T 1191457
 
7.3%
S 1147207
 
7.0%
O 1137342
 
6.9%
N 1043049
 
6.4%
A 1008200
 
6.2%
L 950839
 
5.8%
F 930326
 
5.7%
R 901971
 
5.5%
Other values (20) 4744684
29.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14638071
89.4%
Space Separator 1290531
 
7.9%
Other Punctuation 418636
 
2.6%
Dash Punctuation 20694
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2022326
13.8%
T 1191457
 
8.1%
S 1147207
 
7.8%
O 1137342
 
7.8%
N 1043049
 
7.1%
A 1008200
 
6.9%
L 950839
 
6.5%
F 930326
 
6.4%
R 901971
 
6.2%
I 795899
 
5.4%
Other values (15) 3509455
24.0%
Other Punctuation
ValueCountFrequency (%)
/ 366774
87.6%
& 50170
 
12.0%
, 1692
 
0.4%
Space Separator
ValueCountFrequency (%)
1290531
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14638071
89.4%
Common 1729861
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2022326
13.8%
T 1191457
 
8.1%
S 1147207
 
7.8%
O 1137342
 
7.8%
N 1043049
 
7.1%
A 1008200
 
6.9%
L 950839
 
6.5%
F 930326
 
6.4%
R 901971
 
6.2%
I 795899
 
5.4%
Other values (15) 3509455
24.0%
Common
ValueCountFrequency (%)
1290531
74.6%
/ 366774
 
21.2%
& 50170
 
2.9%
- 20694
 
1.2%
, 1692
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16367932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2022326
12.4%
1290531
 
7.9%
T 1191457
 
7.3%
S 1147207
 
7.0%
O 1137342
 
6.9%
N 1043049
 
6.4%
A 1008200
 
6.2%
L 950839
 
5.8%
F 930326
 
5.7%
R 901971
 
5.5%
Other values (20) 4744684
29.0%

NIBRS Crime Against
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
PROPERTY
441714 
MISCELLANEOUS
173234 
SOCIETY
71748 
PERSON
49957 
PERSON, PROPERTY, OR SOCIETY
44821 

Length

Max length28
Median length8
Mean length10.035804
Min length6

Characters and Unicode

Total characters7842720
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROPERTY
2nd rowPERSON
3rd rowPROPERTY
4th rowSOCIETY
5th rowSOCIETY

Common Values

ValueCountFrequency (%)
PROPERTY 441714
42.5%
MISCELLANEOUS 173234
 
16.7%
SOCIETY 71748
 
6.9%
PERSON 49957
 
4.8%
PERSON, PROPERTY, OR SOCIETY 44821
 
4.3%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.597909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:35.666534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
property 486535
53.1%
miscellaneous 173234
 
18.9%
society 116569
 
12.7%
person 94778
 
10.3%
or 44821
 
4.9%

Most occurring characters

ValueCountFrequency (%)
R 1112669
14.2%
P 1067848
13.6%
E 1044350
13.3%
O 915937
11.7%
T 603104
7.7%
Y 603104
7.7%
S 557815
7.1%
L 346468
 
4.4%
I 289803
 
3.7%
C 289803
 
3.7%
Other values (6) 1011819
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7618615
97.1%
Space Separator 134463
 
1.7%
Other Punctuation 89642
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1112669
14.6%
P 1067848
14.0%
E 1044350
13.7%
O 915937
12.0%
T 603104
7.9%
Y 603104
7.9%
S 557815
7.3%
L 346468
 
4.5%
I 289803
 
3.8%
C 289803
 
3.8%
Other values (4) 787714
10.3%
Space Separator
ValueCountFrequency (%)
134463
100.0%
Other Punctuation
ValueCountFrequency (%)
, 89642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7618615
97.1%
Common 224105
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1112669
14.6%
P 1067848
14.0%
E 1044350
13.7%
O 915937
12.0%
T 603104
7.9%
Y 603104
7.9%
S 557815
7.3%
L 346468
 
4.5%
I 289803
 
3.8%
C 289803
 
3.8%
Other values (4) 787714
10.3%
Common
ValueCountFrequency (%)
134463
60.0%
, 89642
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7842720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1112669
14.2%
P 1067848
13.6%
E 1044350
13.3%
O 915937
11.7%
T 603104
7.7%
Y 603104
7.7%
S 557815
7.1%
L 346468
 
4.4%
I 289803
 
3.7%
C 289803
 
3.7%
Other values (6) 1011819
12.9%

NIBRS Code
Categorical

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
999
193928 
240
125866 
23F
75500 
290
61068 
220
50170 
Other values (45)
274942 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2344422
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row23G
2nd row13C
3rd row220
4th row90Z
5th row520

Common Values

ValueCountFrequency (%)
999 193928
18.7%
240 125866
12.1%
23F 75500
 
7.3%
290 61068
 
5.9%
220 50170
 
4.8%
23H 45400
 
4.4%
90Z 29741
 
2.9%
23G 27717
 
2.7%
35A 23875
 
2.3%
120 22078
 
2.1%
Other values (40) 126131
12.1%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.730449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
999 193928
24.8%
240 125866
16.1%
23f 75500
 
9.7%
290 61068
 
7.8%
220 50170
 
6.4%
23h 45400
 
5.8%
90z 29741
 
3.8%
23g 27717
 
3.5%
35a 23875
 
3.1%
120 22078
 
2.8%
Other values (40) 126131
16.1%

Most occurring characters

ValueCountFrequency (%)
9 706472
30.1%
2 499196
21.3%
0 338891
14.5%
3 240659
 
10.3%
4 125873
 
5.4%
F 78895
 
3.4%
1 71439
 
3.0%
A 49371
 
2.1%
H 45409
 
1.9%
5 35282
 
1.5%
Other values (10) 152935
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2031108
86.6%
Uppercase Letter 313314
 
13.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 706472
34.8%
2 499196
24.6%
0 338891
16.7%
3 240659
 
11.8%
4 125873
 
6.2%
1 71439
 
3.5%
5 35282
 
1.7%
6 9359
 
0.5%
7 3089
 
0.2%
8 848
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 78895
25.2%
A 49371
15.8%
H 45409
14.5%
C 30516
 
9.7%
Z 29741
 
9.5%
G 27761
 
8.9%
B 18139
 
5.8%
E 15969
 
5.1%
D 11091
 
3.5%
J 6422
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2031108
86.6%
Latin 313314
 
13.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 706472
34.8%
2 499196
24.6%
0 338891
16.7%
3 240659
 
11.8%
4 125873
 
6.2%
1 71439
 
3.5%
5 35282
 
1.7%
6 9359
 
0.5%
7 3089
 
0.2%
8 848
 
< 0.1%
Latin
ValueCountFrequency (%)
F 78895
25.2%
A 49371
15.8%
H 45409
14.5%
C 30516
 
9.7%
Z 29741
 
9.5%
G 27761
 
8.9%
B 18139
 
5.8%
E 15969
 
5.1%
D 11091
 
3.5%
J 6422
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2344422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 706472
30.1%
2 499196
21.3%
0 338891
14.5%
3 240659
 
10.3%
4 125873
 
5.4%
F 78895
 
3.4%
1 71439
 
3.0%
A 49371
 
2.1%
H 45409
 
1.9%
5 35282
 
1.5%
Other values (10) 152935
 
6.5%

NIBRS Group
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
A
524804 
C
193928 
B
62742 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters781474
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 524804
50.5%
C 193928
 
18.7%
B 62742
 
6.0%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.784989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T15:41:35.848121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
a 524804
67.2%
c 193928
 
24.8%
b 62742
 
8.0%

Most occurring characters

ValueCountFrequency (%)
A 524804
67.2%
C 193928
 
24.8%
B 62742
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 781474
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 524804
67.2%
C 193928
 
24.8%
B 62742
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 781474
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 524804
67.2%
C 193928
 
24.8%
B 62742
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 524804
67.2%
C 193928
 
24.8%
B 62742
 
8.0%

NIBRS Type
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing257029
Missing (%)24.7%
Memory size7.9 MiB
Coded
440763 
999 - No Coded
193928 
Not Coded
62968 
90Z - No Coded
 
29741
No Coded
 
21023
Other values (8)
 
33051

Length

Max length14
Median length5
Mean length8.3588718
Min length3

Characters and Unicode

Total characters6532241
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoded
2nd rowCoded
3rd rowCoded
4th row90Z - No Coded
5th rowCoded

Common Values

ValueCountFrequency (%)
Coded 440763
42.4%
999 - No Coded 193928
18.7%
Not Coded 62968
 
6.1%
90Z - No Coded 29741
 
2.9%
No Coded 21023
 
2.0%
90E - No Coded 15600
 
1.5%
90D - No Coded 7978
 
0.8%
90J - No Coded 6422
 
0.6%
90F - No Coded 1692
 
0.2%
90C - No Coded 1282
 
0.1%
Other values (3) 77
 
< 0.1%
(Missing) 257029
24.7%

Length

2023-04-21T15:41:35.906448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coded 781424
47.8%
no 277693
 
17.0%
256670
 
15.7%
999 193928
 
11.9%
not 62968
 
3.9%
90z 29741
 
1.8%
90e 15600
 
1.0%
90d 7978
 
0.5%
90j 6422
 
0.4%
90f 1692
 
0.1%
Other values (4) 1359
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 1562848
23.9%
o 1122085
17.2%
854001
13.1%
C 782756
12.0%
e 781424
12.0%
9 644526
9.9%
N 340661
 
5.2%
- 256670
 
3.9%
t 62968
 
1.0%
0 62742
 
1.0%
Other values (8) 61560
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3529325
54.0%
Uppercase Letter 1184977
 
18.1%
Space Separator 854001
 
13.1%
Decimal Number 707268
 
10.8%
Dash Punctuation 256670
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 782756
66.1%
N 340661
28.7%
Z 29741
 
2.5%
E 15600
 
1.3%
D 8028
 
0.7%
J 6422
 
0.5%
F 1692
 
0.1%
O 50
 
< 0.1%
G 18
 
< 0.1%
H 9
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 1562848
44.3%
o 1122085
31.8%
e 781424
22.1%
t 62968
 
1.8%
Decimal Number
ValueCountFrequency (%)
9 644526
91.1%
0 62742
 
8.9%
Space Separator
ValueCountFrequency (%)
854001
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 256670
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4714302
72.2%
Common 1817939
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1562848
33.2%
o 1122085
23.8%
C 782756
16.6%
e 781424
16.6%
N 340661
 
7.2%
t 62968
 
1.3%
Z 29741
 
0.6%
E 15600
 
0.3%
D 8028
 
0.2%
J 6422
 
0.1%
Other values (4) 1769
 
< 0.1%
Common
ValueCountFrequency (%)
854001
47.0%
9 644526
35.5%
- 256670
 
14.1%
0 62742
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6532241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1562848
23.9%
o 1122085
17.2%
854001
13.1%
C 782756
12.0%
e 781424
12.0%
9 644526
9.9%
N 340661
 
5.2%
- 256670
 
3.9%
t 62968
 
1.0%
0 62742
 
1.0%
Other values (8) 61560
 
0.9%

Update Date
Categorical

Distinct3600
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
11:03.0
 
433
53:00.0
 
367
22:14.0
 
356
41:08.0
 
354
40:51.0
 
352
Other values (3595)
1036641 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7269521
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15:06.0
2nd row40:28.0
3rd row12:13.0
4th row30:37.0
5th row50:50.0

Common Values

ValueCountFrequency (%)
11:03.0 433
 
< 0.1%
53:00.0 367
 
< 0.1%
22:14.0 356
 
< 0.1%
41:08.0 354
 
< 0.1%
40:51.0 352
 
< 0.1%
07:04.0 350
 
< 0.1%
40:21.0 348
 
< 0.1%
39:19.0 348
 
< 0.1%
20:40.0 346
 
< 0.1%
51:17.0 346
 
< 0.1%
Other values (3590) 1034903
99.7%

Length

2023-04-21T15:41:35.964616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11:03.0 433
 
< 0.1%
53:00.0 367
 
< 0.1%
22:14.0 356
 
< 0.1%
41:08.0 354
 
< 0.1%
40:51.0 352
 
< 0.1%
07:04.0 350
 
< 0.1%
40:21.0 348
 
< 0.1%
39:19.0 348
 
< 0.1%
20:40.0 346
 
< 0.1%
51:17.0 346
 
< 0.1%
Other values (3590) 1034903
99.7%

Most occurring characters

ValueCountFrequency (%)
0 1589666
21.9%
: 1038503
14.3%
. 1038503
14.3%
4 559380
 
7.7%
5 555465
 
7.6%
3 554038
 
7.6%
2 551682
 
7.6%
1 551426
 
7.6%
8 208429
 
2.9%
9 208331
 
2.9%
Other values (2) 414098
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5192515
71.4%
Other Punctuation 2077006
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1589666
30.6%
4 559380
 
10.8%
5 555465
 
10.7%
3 554038
 
10.7%
2 551682
 
10.6%
1 551426
 
10.6%
8 208429
 
4.0%
9 208331
 
4.0%
7 207497
 
4.0%
6 206601
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 1038503
50.0%
. 1038503
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7269521
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1589666
21.9%
: 1038503
14.3%
. 1038503
14.3%
4 559380
 
7.7%
5 555465
 
7.6%
3 554038
 
7.6%
2 551682
 
7.6%
1 551426
 
7.6%
8 208429
 
2.9%
9 208331
 
2.9%
Other values (2) 414098
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7269521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1589666
21.9%
: 1038503
14.3%
. 1038503
14.3%
4 559380
 
7.7%
5 555465
 
7.6%
3 554038
 
7.6%
2 551682
 
7.6%
1 551426
 
7.6%
8 208429
 
2.9%
9 208331
 
2.9%
Other values (2) 414098
 
5.7%

X Coordinate
Real number (ℝ)

Distinct266915
Distinct (%)25.8%
Missing3738
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2494076.2
Minimum2320290.3
Maximum2593936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:36.145884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2320290.3
5-th percentile2461655.1
Q12478300.1
median2492765
Q32506329.2
95-th percentile2532222
Maximum2593936
Range273645.73
Interquartile range (IQR)28029.088

Descriptive statistics

Standard deviation21836.789
Coefficient of variation (CV)0.0087554621
Kurtosis-0.25910686
Mean2494076.2
Median Absolute Deviation (MAD)14024.468
Skewness0.30461319
Sum2.5807827 × 1012
Variance4.7684537 × 108
MonotonicityNot monotonic
2023-04-21T15:41:36.220017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2492641.468 2252
 
0.2%
2499512.558 1627
 
0.2%
2492356.583 1444
 
0.1%
2475408.364 1441
 
0.1%
2499502.45 1306
 
0.1%
2499425.916 1107
 
0.1%
2503911.443 1044
 
0.1%
2508871.965 785
 
0.1%
2475384.38 720
 
0.1%
2525177.503 682
 
0.1%
Other values (266905) 1022357
98.4%
(Missing) 3738
 
0.4%
ValueCountFrequency (%)
2320290.301 1
< 0.1%
2320426.844 1
< 0.1%
2361600.885 1
< 0.1%
2375762.08 1
< 0.1%
2410620.597 1
< 0.1%
2415423.802 1
< 0.1%
2416996.921 1
< 0.1%
2417185.404 1
< 0.1%
2418797.993 1
< 0.1%
2419584.35 1
< 0.1%
ValueCountFrequency (%)
2593936.036 1
< 0.1%
2591024.056 1
< 0.1%
2590095.833 1
< 0.1%
2588312.228 1
< 0.1%
2588264.158 1
< 0.1%
2588000.55 1
< 0.1%
2587802.432 1
< 0.1%
2587600.431 1
< 0.1%
2587516.788 2
< 0.1%
2586995.671 1
< 0.1%

Y Cordinate
Real number (ℝ)

Distinct264538
Distinct (%)25.6%
Missing3738
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6978819.7
Minimum6892267.6
Maximum7121761.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:36.303181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6892267.6
5-th percentile6928120.8
Q16955997
median6976496.7
Q37001833.8
95-th percentile7030013.7
Maximum7121761.9
Range229494.34
Interquartile range (IQR)45836.811

Descriptive statistics

Standard deviation31203.589
Coefficient of variation (CV)0.0044711843
Kurtosis-0.52155997
Mean6978819.7
Median Absolute Deviation (MAD)23492.541
Skewness0.25475299
Sum7.2214384 × 1012
Variance9.7366395 × 108
MonotonicityNot monotonic
2023-04-21T15:41:36.382280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6966516.799 1715
 
0.2%
7002979.497 1464
 
0.1%
6966513.775 1445
 
0.1%
6993943.004 1441
 
0.1%
7002995.817 1306
 
0.1%
7002954.29 1107
 
0.1%
6966516.8 1070
 
0.1%
6970105.639 1044
 
0.1%
6950900.433 933
 
0.1%
6964406.794 836
 
0.1%
Other values (264528) 1022404
98.4%
(Missing) 3738
 
0.4%
ValueCountFrequency (%)
6892267.582 1
< 0.1%
6892808.098 1
< 0.1%
6894036.464 1
< 0.1%
6898157.859 1
< 0.1%
6899621.249 1
< 0.1%
6899907.327 1
< 0.1%
6900614.032 1
< 0.1%
6901088.368 1
< 0.1%
6902804.595 1
< 0.1%
6903244.452 1
< 0.1%
ValueCountFrequency (%)
7121761.923 1
< 0.1%
7115446.042 1
< 0.1%
7101520.886 1
< 0.1%
7100757.507 2
< 0.1%
7088151.535 1
< 0.1%
7087270.019 1
< 0.1%
7084250.546 1
< 0.1%
7083090.217 1
< 0.1%
7081537.997 2
< 0.1%
7078378.613 1
< 0.1%

Zip Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct194
Distinct (%)< 0.1%
Missing4024
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean75224.477
Minimum0
Maximum98004
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2023-04-21T15:41:36.458507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75203
Q175214
median75224
Q375236
95-th percentile75252
Maximum98004
Range98004
Interquartile range (IQR)22

Descriptive statistics

Standard deviation155.76013
Coefficient of variation (CV)0.0020706044
Kurtosis121842.39
Mean75224.477
Median Absolute Deviation (MAD)12
Skewness-297.4925
Sum7.7818142 × 1010
Variance24261.218
MonotonicityNot monotonic
2023-04-21T15:41:36.536301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75217 56065
 
5.4%
75216 50776
 
4.9%
75228 49819
 
4.8%
75220 49531
 
4.8%
75243 49199
 
4.7%
75211 43094
 
4.1%
75204 37347
 
3.6%
75227 35398
 
3.4%
75215 34891
 
3.4%
75231 33422
 
3.2%
Other values (184) 594937
57.3%
ValueCountFrequency (%)
0 1
< 0.1%
11576 1
< 0.1%
16066 1
< 0.1%
30305 1
< 0.1%
33455 1
< 0.1%
33896 1
< 0.1%
40517 1
< 0.1%
48232 1
< 0.1%
50704 1
< 0.1%
57238 1
< 0.1%
ValueCountFrequency (%)
98004 1
< 0.1%
97224 1
< 0.1%
95207 1
< 0.1%
91803 1
< 0.1%
91601 1
< 0.1%
90806 1
< 0.1%
90033 1
< 0.1%
89148 1
< 0.1%
79745 1
< 0.1%
78681 1
< 0.1%

City
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct147
Distinct (%)< 0.1%
Missing5326
Missing (%)0.5%
Memory size7.9 MiB
DALLAS
1026845 
Dallas
 
4323
GARLAND
 
206
DAL
 
150
RICHARDSON
 
146
Other values (142)
 
1507

Length

Max length16
Median length6
Mean length6.0031669
Min length1

Characters and Unicode

Total characters6202334
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)< 0.1%

Sample

1st rowDALLAS
2nd rowDALLAS
3rd rowDALLAS
4th rowDALLAS
5th rowDALLAS

Common Values

ValueCountFrequency (%)
DALLAS 1026845
98.9%
Dallas 4323
 
0.4%
GARLAND 206
 
< 0.1%
DAL 150
 
< 0.1%
RICHARDSON 146
 
< 0.1%
DLS 135
 
< 0.1%
MESQUITE 122
 
< 0.1%
ROWLETT 106
 
< 0.1%
DUNCANVILLE 106
 
< 0.1%
DESOTO 85
 
< 0.1%
Other values (137) 953
 
0.1%
(Missing) 5326
 
0.5%

Length

2023-04-21T15:41:36.613386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dallas 1031176
99.8%
garland 206
 
< 0.1%
dal 150
 
< 0.1%
richardson 146
 
< 0.1%
dls 135
 
< 0.1%
mesquite 122
 
< 0.1%
rowlett 106
 
< 0.1%
duncanville 106
 
< 0.1%
grand 94
 
< 0.1%
desoto 85
 
< 0.1%
Other values (155) 1105
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 2055635
33.1%
L 2055577
33.1%
D 1032450
16.6%
S 1027815
16.6%
a 8646
 
0.1%
l 8646
 
0.1%
s 4323
 
0.1%
R 1478
 
< 0.1%
N 1169
 
< 0.1%
I 933
 
< 0.1%
Other values (26) 5662
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6180442
99.6%
Lowercase Letter 21615
 
0.3%
Space Separator 254
 
< 0.1%
Decimal Number 20
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2055635
33.3%
L 2055577
33.3%
D 1032450
16.7%
S 1027815
16.6%
R 1478
 
< 0.1%
N 1169
 
< 0.1%
I 933
 
< 0.1%
E 932
 
< 0.1%
O 865
 
< 0.1%
T 653
 
< 0.1%
Other values (14) 2935
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
5 5
25.0%
2 5
25.0%
0 4
20.0%
7 3
15.0%
4 2
 
10.0%
1 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
a 8646
40.0%
l 8646
40.0%
s 4323
20.0%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6202057
> 99.9%
Common 277
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2055635
33.1%
L 2055577
33.1%
D 1032450
16.6%
S 1027815
16.6%
a 8646
 
0.1%
l 8646
 
0.1%
s 4323
 
0.1%
R 1478
 
< 0.1%
N 1169
 
< 0.1%
I 933
 
< 0.1%
Other values (17) 5385
 
0.1%
Common
ValueCountFrequency (%)
254
91.7%
5 5
 
1.8%
2 5
 
1.8%
0 4
 
1.4%
7 3
 
1.1%
, 2
 
0.7%
4 2
 
0.7%
1 1
 
0.4%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6202334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2055635
33.1%
L 2055577
33.1%
D 1032450
16.6%
S 1027815
16.6%
a 8646
 
0.1%
l 8646
 
0.1%
s 4323
 
0.1%
R 1478
 
< 0.1%
N 1169
 
< 0.1%
I 933
 
< 0.1%
Other values (26) 5662
 
0.1%

State
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct38
Distinct (%)< 0.1%
Missing15296
Missing (%)1.5%
Memory size7.9 MiB
TX
1022065 
T
 
904
TN
 
110
UT
 
35
TC
 
31
Other values (33)
 
62

Length

Max length2
Median length2
Mean length1.9991155
Min length1

Characters and Unicode

Total characters2045509
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowTX
2nd rowTX
3rd rowTX
4th rowTX
5th rowTX

Common Values

ValueCountFrequency (%)
TX 1022065
98.4%
T 904
 
0.1%
TN 110
 
< 0.1%
UT 35
 
< 0.1%
TC 31
 
< 0.1%
DE 9
 
< 0.1%
YX 5
 
< 0.1%
WA 4
 
< 0.1%
UK 4
 
< 0.1%
DC 3
 
< 0.1%
Other values (28) 37
 
< 0.1%
(Missing) 15296
 
1.5%

Length

2023-04-21T15:41:36.680824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx 1022065
99.9%
t 904
 
0.1%
tn 110
 
< 0.1%
ut 35
 
< 0.1%
tc 31
 
< 0.1%
de 9
 
< 0.1%
yx 5
 
< 0.1%
wa 4
 
< 0.1%
uk 4
 
< 0.1%
dc 3
 
< 0.1%
Other values (28) 37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 1023153
50.0%
X 1022075
50.0%
N 112
 
< 0.1%
U 39
 
< 0.1%
C 39
 
< 0.1%
A 17
 
< 0.1%
D 14
 
< 0.1%
E 9
 
< 0.1%
Y 9
 
< 0.1%
R 9
 
< 0.1%
Other values (12) 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2045509
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1023153
50.0%
X 1022075
50.0%
N 112
 
< 0.1%
U 39
 
< 0.1%
C 39
 
< 0.1%
A 17
 
< 0.1%
D 14
 
< 0.1%
E 9
 
< 0.1%
Y 9
 
< 0.1%
R 9
 
< 0.1%
Other values (12) 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2045509
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1023153
50.0%
X 1022075
50.0%
N 112
 
< 0.1%
U 39
 
< 0.1%
C 39
 
< 0.1%
A 17
 
< 0.1%
D 14
 
< 0.1%
E 9
 
< 0.1%
Y 9
 
< 0.1%
R 9
 
< 0.1%
Other values (12) 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2045509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1023153
50.0%
X 1022075
50.0%
N 112
 
< 0.1%
U 39
 
< 0.1%
C 39
 
< 0.1%
A 17
 
< 0.1%
D 14
 
< 0.1%
E 9
 
< 0.1%
Y 9
 
< 0.1%
R 9
 
< 0.1%
Other values (12) 33
 
< 0.1%

Location1
Categorical

Distinct244593
Distinct (%)23.6%
Missing3433
Missing (%)0.3%
Memory size7.9 MiB
1400 S LAMAR ST DALLAS, TX 75215 (32.767362, -96.795092)
 
2601
8687 N CENTRAL EXPY DALLAS, TX 75225 (32.86875, -96.770691)
 
2320
8008 HERB KELLEHER WAY DALLAS, TX 75235 (32.85262, -96.85281)
 
2164
8687 N CENTRAL SERV SB DALLAS, TX 75225 (32.86875, -96.770691)
 
1266
1600 FUN WAY DALLAS, TX 75210
 
1172
Other values (244588)
1025547 

Length

Max length91
Median length79
Mean length57.083724
Min length10

Characters and Unicode

Total characters59085650
Distinct characters51
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130864 ?
Unique (%)12.6%

Sample

1st row7152 FAIR OAKS AVE DALLAS, TX 75231 (32.87309, -96.75785)
2nd row2525 PLEASANT DR DALLAS, TX 75227 (32.75592, -96.67864)
3rd row10443 N CENTRAL EXPY DALLAS, TX 75231 (32.88869, -96.77017)
4th row6336 ALCORN AVE DALLAS, TX 75217 (32.71761, -96.71109)
5th row226 LONGBRANCH LN DALLAS, TX 75217 (32.71056, -96.69018)

Common Values

ValueCountFrequency (%)
1400 S LAMAR ST DALLAS, TX 75215 (32.767362, -96.795092) 2601
 
0.3%
8687 N CENTRAL EXPY DALLAS, TX 75225 (32.86875, -96.770691) 2320
 
0.2%
8008 HERB KELLEHER WAY DALLAS, TX 75235 (32.85262, -96.85281) 2164
 
0.2%
8687 N CENTRAL SERV SB DALLAS, TX 75225 (32.86875, -96.770691) 1266
 
0.1%
1600 FUN WAY DALLAS, TX 75210 1172
 
0.1%
1521 N COCKRELL HILL RD DALLAS, TX 75211 (32.763549, -96.895452) 990
 
0.1%
9915 E NORTHWEST HWY DALLAS, TX 75238 (32.863181, -96.715202) 986
 
0.1%
7401 SAMUELL BLVD DALLAS, TX 75228 (32.792547, -96.687653) 962
 
0.1%
9301 FOREST LN DALLAS, TX 75243 (32.909208, -96.73876) 951
 
0.1%
725 N JIM MILLER RD DALLAS, TX 75217 (32.725715, -96.700115) 935
 
0.1%
Other values (244583) 1020723
98.3%
(Missing) 3433
 
0.3%

Length

2023-04-21T15:41:36.773806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dallas 1038871
 
11.8%
tx 1022071
 
11.6%
rd 195614
 
2.2%
dr 175562
 
2.0%
st 171633
 
2.0%
ave 161092
 
1.8%
ln 97933
 
1.1%
n 82628
 
0.9%
blvd 81415
 
0.9%
s 77127
 
0.9%
Other values (285305) 5695311
64.7%

Most occurring characters

ValueCountFrequency (%)
5718859
 
9.7%
2 3756879
 
6.4%
7 3266973
 
5.5%
A 2948077
 
5.0%
L 2913624
 
4.9%
6 2665543
 
4.5%
3 2606696
 
4.4%
9 2509713
 
4.2%
5 2504159
 
4.2%
8 2180437
 
3.7%
Other values (41) 28014690
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25016823
42.3%
Uppercase Letter 19185836
32.5%
Space Separator 5718859
 
9.7%
Other Punctuation 4060588
 
6.9%
Control 2045328
 
3.5%
Dash Punctuation 1012281
 
1.7%
Open Punctuation 1012160
 
1.7%
Close Punctuation 1012160
 
1.7%
Lowercase Letter 21615
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2948077
15.4%
L 2913624
15.2%
D 1747766
9.1%
S 1728407
9.0%
T 1693187
8.8%
R 1094747
 
5.7%
E 1091038
 
5.7%
X 1054995
 
5.5%
N 814922
 
4.2%
O 590725
 
3.1%
Other values (16) 3508348
18.3%
Decimal Number
ValueCountFrequency (%)
2 3756879
15.0%
7 3266973
13.1%
6 2665543
10.7%
3 2606696
10.4%
9 2509713
10.0%
5 2504159
10.0%
8 2180437
8.7%
1 2074073
8.3%
0 1954260
7.8%
4 1498090
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 2035377
50.1%
. 2024617
49.9%
& 544
 
< 0.1%
/ 38
 
< 0.1%
# 9
 
< 0.1%
' 2
 
< 0.1%
; 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 8646
40.0%
l 8646
40.0%
s 4323
20.0%
Space Separator
ValueCountFrequency (%)
5718859
100.0%
Control
ValueCountFrequency (%)
2045328
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1012281
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1012160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1012160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39878199
67.5%
Latin 19207451
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2948077
15.3%
L 2913624
15.2%
D 1747766
9.1%
S 1728407
9.0%
T 1693187
8.8%
R 1094747
 
5.7%
E 1091038
 
5.7%
X 1054995
 
5.5%
N 814922
 
4.2%
O 590725
 
3.1%
Other values (19) 3529963
18.4%
Common
ValueCountFrequency (%)
5718859
14.3%
2 3756879
 
9.4%
7 3266973
 
8.2%
6 2665543
 
6.7%
3 2606696
 
6.5%
9 2509713
 
6.3%
5 2504159
 
6.3%
8 2180437
 
5.5%
1 2074073
 
5.2%
2045328
 
5.1%
Other values (12) 10549539
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59085650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5718859
 
9.7%
2 3756879
 
6.4%
7 3266973
 
5.5%
A 2948077
 
5.0%
L 2913624
 
4.9%
6 2665543
 
4.5%
3 2606696
 
4.4%
9 2509713
 
4.2%
5 2504159
 
4.2%
8 2180437
 
3.7%
Other values (41) 28014690
47.4%

Interactions

2023-04-21T15:40:12.399532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:37.695838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-21T15:39:56.732662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:59.299818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:01.764130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:04.493637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:06.871419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:09.680538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:12.084431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:14.508793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:40.065127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:42.262797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:44.676054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:47.015993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:49.545460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:51.989848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:54.383716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:56.888519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:39:59.460293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:01.920620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:04.700611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:06.982558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:09.850460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-21T15:40:12.243960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-21T15:41:36.904387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Year of IncidentReporting AreaBeatSectorYear1 of OccurrenceDay1 of the YearYear2 of OccurrenceDay2 of the YearOffense Entered YearOffense Entered Date/TimeCriminal Justice Information Service CodeUCR CodeX CoordinateY CordinateZip CodeWatchType LocationType of PropertyDivisionCouncil DistrictTarget Area Action GridsCommunityMonth1 of OccurenceDay1 of the WeekMonth2 of OccurenceDay2 of the WeekOffense Entered MonthOffense Entered Day of the WeekSpecial Report (Pre-RMS)Person Involvement TypeVictim TypeVictim RaceVictim EthnicityVictim GenderInvestigating Unit 1Investigating Unit 2Offense StatusUCR DispositionHate CrimeHate Crime DescriptionWeapon UsedGang Related OffenseDrug Related IstevencidentUCR Offense NameUCR Offense DescriptionOffense TypeNIBRS CrimeNIBRS Crime CategoryNIBRS Crime AgainstNIBRS CodeNIBRS GroupNIBRS TypeState
Year of Incident1.000-0.006-0.014-0.0130.997-0.1010.999-0.1051.000-0.1120.1110.040-0.0260.029-0.0190.0530.1340.0860.0410.0250.0510.0420.1150.0050.1180.0070.1220.0100.4830.0180.0690.0410.0330.0110.0440.0870.0780.0700.7630.0560.3860.2630.0540.0790.1730.0300.0680.0580.0500.0740.0520.0470.007
Reporting Area-0.0061.0000.4090.409-0.006-0.002-0.006-0.002-0.006-0.0020.0110.004-0.385-0.3010.1230.0230.1440.0820.5930.5270.7160.7930.0100.0090.0100.0090.0100.0060.3220.0090.0430.1130.0730.0320.0300.4090.0290.0290.1050.0090.0390.0160.0260.0870.0740.0580.0730.0610.0530.0700.0500.0400.012
Beat-0.0140.4091.0001.000-0.014-0.005-0.014-0.005-0.014-0.005-0.007-0.014-0.441-0.1270.3870.0250.1570.1000.8170.7310.9340.9220.0090.0110.0090.0100.0090.0080.2880.0110.0380.1440.1550.0380.0510.5900.0280.0230.1200.0090.0440.0220.0250.0830.0740.0500.0790.0680.0530.0750.0470.0360.011
Sector-0.0130.4091.0001.000-0.013-0.005-0.013-0.005-0.013-0.005-0.007-0.015-0.444-0.1260.3870.0270.1580.0940.8170.7320.8900.9220.0090.0110.0090.0100.0090.0080.2730.0110.0380.1430.1540.0380.0510.5560.0300.0250.1320.0090.0440.0220.0250.0840.0750.0510.0750.0640.0540.0710.0500.0350.082
Year1 of Occurrence0.997-0.006-0.014-0.0131.000-0.1060.999-0.1050.997-0.1050.1100.040-0.0250.028-0.0190.0400.1160.1470.0270.0200.0440.0390.0300.0030.0290.0050.0300.0080.2780.0130.0540.0310.0280.0100.0300.0760.0350.0460.7420.0460.2430.2320.0480.0480.0470.0180.0520.0440.0310.0560.0240.0320.005
Day1 of the Year-0.101-0.002-0.005-0.005-0.1061.000-0.1040.973-0.1010.946-0.010-0.0210.0030.004-0.0010.0080.0290.0140.0110.0080.0130.0160.8360.0050.8190.0050.7990.0060.4390.0050.0120.0130.0080.0150.0130.0200.0190.0160.0590.0050.0720.0150.0110.0270.0570.0270.0210.0180.0220.0200.0270.0140.002
Year2 of Occurrence0.999-0.006-0.014-0.0130.999-0.1041.000-0.1080.999-0.1080.1110.040-0.0260.028-0.0190.0470.1180.1100.0270.0210.0500.0490.0660.0030.0680.0050.0690.0070.6910.0150.0570.0380.0280.0100.0310.0840.0350.0320.7500.0470.4780.2320.0490.0500.0530.0220.0500.0430.0310.0540.0250.0330.005
Day2 of the Year-0.105-0.002-0.005-0.005-0.1050.973-0.1081.000-0.1050.970-0.010-0.0200.0030.004-0.0010.0090.0290.0140.0110.0080.0130.0160.8190.0050.8370.0060.8140.0060.4430.0050.0120.0120.0080.0150.0130.0210.0190.0160.0670.0050.0730.0160.0110.0270.0580.0280.0210.0180.0220.0200.0270.0140.001
Offense Entered Year1.000-0.006-0.014-0.0130.997-0.1010.999-0.1051.000-0.1120.1110.040-0.0260.029-0.0190.0530.1340.0800.0410.0260.0470.0390.1150.0060.1180.0070.1220.0110.4590.0190.0690.0420.0330.0120.0460.0880.0790.0660.7640.0570.3640.2680.0540.0820.1730.0310.0770.0600.0510.0850.0520.0520.008
Offense Entered Date/Time-0.112-0.002-0.005-0.005-0.1050.946-0.1080.970-0.1121.000-0.009-0.0200.0040.004-0.0010.0080.0290.0220.0130.0080.0130.0160.7980.0040.8140.0050.8370.0060.4440.0050.0120.0130.0080.0150.0130.0220.0190.0160.2890.0050.0740.0170.0130.0270.0580.0280.0220.0190.0230.0200.0280.0150.001
Criminal Justice Information Service Code0.1110.011-0.007-0.0070.110-0.0100.111-0.0100.111-0.0091.0000.8720.002-0.023-0.0150.0980.1460.1120.0380.0440.0650.0630.0130.0230.0130.0260.0140.0320.2390.2100.2590.0770.0710.0610.3110.4560.2000.1740.1640.0260.2960.2450.2930.7770.7350.7430.8240.8170.6960.7900.6540.4090.000
UCR Code0.0400.004-0.014-0.0150.040-0.0210.040-0.0200.040-0.0200.8721.000-0.002-0.030-0.0330.0990.1840.1640.0550.0540.0710.0700.0220.0310.0220.0340.0220.0400.2610.1520.2820.2150.0750.1330.5370.5550.4200.3330.2550.0350.1470.0610.1390.9680.9460.8170.7740.7370.7290.7270.8390.5280.006
X Coordinate-0.026-0.385-0.441-0.444-0.0250.003-0.0260.003-0.0260.0040.002-0.0021.0000.0460.0430.0130.1000.0550.4830.5480.6890.7160.0050.0040.0060.0050.0050.0040.3000.0120.0200.0700.1360.0160.0260.2420.0190.0160.0280.0000.0260.0090.0130.0600.0550.0310.0400.0350.0240.0380.0310.0230.004
Y Cordinate0.029-0.301-0.127-0.1260.0280.0040.0280.0040.0290.004-0.023-0.0300.0461.0000.3270.0140.1170.0800.4720.6490.7270.9790.0060.0080.0070.0080.0060.0070.2680.0060.0310.1160.1170.0250.0230.3090.0240.0200.0790.0060.0310.0160.0210.0580.0520.0410.0550.0480.0420.0530.0330.0250.008
Zip Code-0.0190.1230.3870.387-0.019-0.001-0.019-0.001-0.019-0.001-0.015-0.0330.0430.3271.0000.0000.0111.0000.0300.0000.0021.0000.0010.0010.0000.0000.0010.0001.0000.0000.0000.0000.0000.0000.0000.0220.0090.0071.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0030.0000.0050.0000.759
Watch0.0530.0230.0250.0270.0400.0080.0470.0090.0530.0080.0980.0990.0130.0140.0001.0000.1140.1090.0550.0330.0680.0690.0080.0380.0090.0450.0080.0370.2730.0250.0500.0230.0210.0250.0330.1000.0510.0510.1920.0240.1010.0580.0360.1950.1480.0950.1630.1310.1090.1490.0950.0940.000
Type Location0.1340.1440.1570.1580.1160.0290.1180.0290.1340.0290.1460.1840.1000.1170.0110.1141.0000.5120.1220.1110.1160.1210.0300.0400.0300.0430.0290.0430.3020.0530.3130.0870.0890.1070.1420.1100.1280.1050.2610.0210.0660.1140.1000.1740.1450.2100.1510.1480.2200.1420.2180.1270.009
Type of Property0.0860.0820.1000.0940.1470.0140.1100.0140.0800.0220.1120.1640.0550.0801.0000.1090.5121.0000.1200.0730.0810.0890.0140.0220.0140.0240.0210.0310.2420.0600.3090.0410.0400.0510.0740.1090.1180.0890.4270.0240.0880.0510.3560.1960.1420.1420.2500.1770.1620.2200.1680.0970.007
Division0.0410.5930.8170.8170.0270.0110.0270.0110.0410.0130.0380.0550.4830.4720.0300.0550.1220.1201.0000.8120.6620.7260.0130.0110.0140.0120.0140.0120.2710.0100.0380.1280.1480.0380.0510.4630.0320.0260.1550.0070.0370.0220.0290.0690.0620.0540.0600.0520.0530.0570.0480.0300.061
Council District0.0250.5270.7310.7320.0200.0080.0210.0080.0260.0080.0440.0540.5480.6490.0000.0330.1110.0730.8121.0000.7360.8670.0090.0110.0090.0110.0080.0090.2760.0120.0400.1410.1810.0390.0430.3220.0290.0240.1500.0050.0320.0270.0270.0640.0570.0560.0570.0490.0560.0540.0440.0300.009
Target Area Action Grids0.0510.7160.9340.8900.0440.0130.0500.0130.0470.0130.0650.0710.6890.7270.0020.0680.1160.0810.6620.7361.0000.7910.0120.0270.0120.0290.0120.0250.3070.0240.0650.1850.2850.0740.0620.3040.0400.0330.1590.0090.0320.0470.0570.0680.0580.0890.0640.0550.0870.0610.0670.0450.022
Community0.0420.7930.9220.9220.0390.0160.0490.0160.0390.0160.0630.0700.7160.9791.0000.0690.1210.0890.7260.8670.7911.0000.0150.0130.0150.0140.0160.0160.2470.0260.0650.2080.3460.0920.0530.3200.0460.0370.0000.0050.0350.0530.0610.0800.0640.0920.0660.0550.0870.0610.0720.0490.053
Month1 of Occurence0.1150.0100.0090.0090.0300.8360.0660.8190.1150.7980.0130.0220.0050.0060.0010.0080.0300.0140.0130.0090.0120.0151.0000.0080.9640.0070.9240.0070.4220.0050.0130.0120.0090.0130.0130.0190.0170.0150.0780.0050.0670.0160.0110.0250.0550.0280.0190.0170.0230.0180.0280.0130.002
Day1 of the Week0.0050.0090.0110.0110.0030.0050.0030.0050.0060.0040.0230.0310.0040.0080.0010.0380.0400.0220.0110.0110.0270.0130.0081.0000.0070.7230.0060.5780.1830.0040.0200.0130.0140.0090.0230.0330.0160.0160.1110.0050.0210.0070.0070.0420.0400.0350.0410.0380.0330.0370.0360.0270.002
Month2 of Occurence0.1180.0100.0090.0090.0290.8190.0680.8370.1180.8140.0130.0220.0060.0070.0000.0090.0300.0140.0140.0090.0120.0150.9640.0071.0000.0090.9540.0080.4250.0050.0130.0120.0080.0130.0130.0190.0180.0150.0860.0050.0680.0160.0120.0250.0550.0290.0190.0170.0230.0180.0280.0130.002
Day2 of the Week0.0070.0090.0100.0100.0050.0050.0050.0060.0070.0050.0260.0340.0050.0080.0000.0450.0430.0240.0120.0110.0290.0140.0070.7230.0091.0000.0070.7600.1960.0040.0260.0120.0120.0090.0250.0370.0180.0190.0940.0070.0230.0090.0080.0450.0430.0370.0440.0410.0360.0390.0400.0280.002
Offense Entered Month0.1220.0100.0090.0090.0300.7990.0690.8140.1220.8370.0140.0220.0050.0060.0010.0080.0290.0210.0140.0080.0120.0160.9240.0060.9540.0071.0000.0090.3980.0050.0130.0120.0080.0130.0130.0200.0170.0150.2930.0050.0700.0170.0140.0250.0560.0290.0200.0180.0240.0190.0290.0140.002
Offense Entered Day of the Week0.0100.0060.0080.0080.0080.0060.0070.0060.0110.0060.0320.0400.0040.0070.0000.0370.0430.0310.0120.0090.0250.0160.0070.5780.0080.7600.0091.0000.1460.0050.0310.0140.0170.0090.0250.0420.0240.0240.2730.0080.0270.0090.0110.0520.0490.0400.0510.0480.0440.0460.0460.0320.001
Special Report (Pre-RMS)0.4830.3220.2880.2730.2780.4390.6910.4430.4590.4440.2390.2610.3000.2681.0000.2730.3020.2420.2710.2760.3070.2470.4220.1830.4250.1960.3980.1461.0000.1300.1970.1950.1330.3030.4140.2370.3120.2360.3570.0420.2960.2910.0990.2440.2380.4780.2700.2580.4110.2570.5070.2810.047
Person Involvement Type0.0180.0090.0110.0110.0130.0050.0150.0050.0190.0050.2100.1520.0120.0060.0000.0250.0530.0600.0100.0120.0240.0260.0050.0040.0050.0040.0050.0050.1301.0000.0420.0290.0270.0340.0400.1700.0250.0240.0000.0030.0150.0230.0190.2180.2180.1420.2670.2670.1010.2670.1120.0840.000
Victim Type0.0690.0430.0380.0380.0540.0120.0570.0120.0690.0120.2590.2820.0200.0310.0000.0500.3130.3090.0380.0400.0650.0650.0130.0200.0130.0260.0130.0310.1970.0421.0000.3520.0440.0530.1060.2060.1920.1670.1390.0180.1160.0760.2400.3950.3430.3680.3560.3300.3900.3370.3510.2080.000
Victim Race0.0410.1130.1440.1430.0310.0130.0380.0120.0420.0130.0770.2150.0700.1160.0000.0230.0870.0410.1280.1410.1850.2080.0120.0130.0120.0120.0120.0140.1950.0290.3521.0000.8490.2770.0950.1330.0500.0410.0490.0140.0540.0410.0550.2280.2230.1620.1180.1150.0870.1200.0690.1030.000
Victim Ethnicity0.0330.0730.1550.1540.0280.0080.0280.0080.0330.0080.0710.0750.1360.1170.0000.0210.0890.0400.1480.1810.2850.3460.0090.0140.0080.0120.0080.0170.1330.0270.0440.8491.0000.2860.0430.1500.0420.0420.0000.0100.0510.0220.0130.1280.1190.0580.1240.1130.0730.1140.0570.0880.000
Victim Gender0.0110.0320.0380.0380.0100.0150.0100.0150.0120.0150.0610.1330.0160.0250.0000.0250.1070.0510.0380.0390.0740.0920.0130.0090.0130.0090.0130.0090.3030.0340.0530.2770.2861.0000.0710.1230.0260.0250.0540.0030.0900.0230.0130.1860.1830.1200.1140.0950.0450.1100.0470.0620.000
Investigating Unit 10.0440.0300.0510.0510.0300.0130.0310.0130.0460.0130.3110.5370.0260.0230.0000.0330.1420.0740.0510.0430.0620.0530.0130.0230.0130.0250.0130.0250.4140.0400.1060.0950.0430.0711.0000.9360.0370.0390.0520.0110.1900.1210.0410.5370.5310.3850.4340.4270.4160.3070.3770.2890.000
Investigating Unit 20.0870.4090.5900.5560.0760.0200.0840.0210.0880.0220.4560.5550.2420.3090.0220.1000.1100.1090.4630.3220.3040.3200.0190.0330.0190.0370.0200.0420.2370.1700.2060.1330.1500.1230.9361.0000.3320.2710.2910.0270.1620.4340.0960.4090.4060.5610.3400.4340.6460.3400.5420.3820.015
Offense Status0.0780.0290.0280.0300.0350.0190.0350.0190.0790.0190.2000.4200.0190.0240.0090.0510.1280.1180.0320.0290.0400.0460.0170.0160.0180.0180.0170.0240.3120.0250.1920.0500.0420.0260.0370.3321.0000.8070.2480.0150.0870.0350.1230.5080.4930.2530.3340.3310.3500.3230.3820.2670.009
UCR Disposition0.0700.0290.0230.0250.0460.0160.0320.0160.0660.0160.1740.3330.0160.0200.0070.0510.1050.0890.0260.0240.0330.0370.0150.0160.0150.0190.0150.0240.2360.0240.1670.0410.0420.0250.0390.2710.8071.0000.2580.0120.0660.0350.1230.3800.3690.2530.2900.2880.3510.2830.3840.2350.005
Hate Crime0.7630.1050.1200.1320.7420.0590.7500.0670.7640.2890.1640.2550.0280.0791.0000.1920.2610.4270.1550.1500.1590.0000.0780.1110.0860.0940.2930.2730.3570.0000.1390.0490.0000.0540.0520.2910.2480.2581.0000.3410.5460.5150.1430.4670.4550.2010.0920.1280.1320.1000.0470.0480.018
Hate Crime Description0.0560.0090.0090.0090.0460.0050.0470.0050.0570.0050.0260.0350.0000.0060.0000.0240.0210.0240.0070.0050.0090.0050.0050.0050.0050.0070.0050.0080.0420.0030.0180.0140.0100.0030.0110.0270.0150.0120.3411.0000.0180.0860.1790.0350.0330.0380.0190.0170.0260.0190.0210.0110.000
Weapon Used0.3860.0390.0440.0440.2430.0720.4780.0730.3640.0740.2960.1470.0260.0310.0000.1010.0660.0880.0370.0320.0320.0350.0670.0210.0680.0230.0700.0270.2960.0150.1160.0540.0510.0900.1900.1620.0870.0660.5460.0181.0000.2800.0500.1730.1690.1500.2210.1800.3110.2180.2320.1330.003
Gang Related Offense0.2630.0160.0220.0220.2320.0150.2320.0160.2680.0170.2450.0610.0090.0160.0000.0580.1140.0510.0220.0270.0470.0530.0160.0070.0160.0090.0170.0090.2910.0230.0760.0410.0220.0230.1210.4340.0350.0350.5150.0860.2801.0000.2050.1290.1290.0400.3410.3330.2840.3400.1330.1230.010
Drug Related Istevencident0.0540.0260.0250.0250.0480.0110.0490.0110.0540.0130.2930.1390.0130.0210.0000.0360.1000.3560.0290.0270.0570.0610.0110.0070.0120.0080.0140.0110.0990.0190.2400.0550.0130.0130.0410.0960.1230.1230.1430.1790.0500.2051.0000.2320.2300.1050.5240.5140.3310.5230.0510.1290.000
UCR Offense Name0.0790.0870.0830.0840.0480.0270.0500.0270.0820.0270.7770.9680.0600.0580.0000.1950.1740.1960.0690.0640.0680.0800.0250.0420.0250.0450.0250.0520.2440.2180.3950.2280.1280.1860.5370.4090.5080.3800.4670.0350.1730.1290.2321.0000.8750.9610.7670.8700.9420.7510.9290.7850.008
UCR Offense Description0.1730.0740.0740.0750.0470.0570.0530.0580.1730.0580.7350.9460.0550.0520.0000.1480.1450.1420.0620.0570.0580.0640.0550.0400.0550.0430.0560.0490.2380.2180.3430.2230.1190.1830.5310.4060.4930.3690.4550.0330.1690.1290.2300.8751.0000.9610.7600.8970.9230.7370.9280.7220.002
Offense Type0.0300.0580.0500.0510.0180.0270.0220.0280.0310.0280.7430.8170.0310.0410.0020.0950.2100.1420.0540.0560.0890.0920.0280.0350.0290.0370.0290.0400.4780.1420.3680.1620.0580.1200.3850.5610.2530.2530.2010.0380.1500.0400.1050.9610.9611.0000.9400.9270.7710.8850.6590.7040.006
NIBRS Crime0.0680.0730.0790.0750.0520.0210.0500.0210.0770.0220.8240.7740.0400.0550.0000.1630.1510.2500.0600.0570.0640.0660.0190.0410.0190.0440.0200.0510.2700.2670.3560.1180.1240.1140.4340.3400.3340.2900.0920.0190.2210.3410.5240.7670.7600.9401.0001.0001.0000.9961.0000.8720.000
NIBRS Crime Category0.0580.0610.0680.0640.0440.0180.0430.0180.0600.0190.8170.7370.0350.0480.0000.1310.1480.1770.0520.0490.0550.0550.0170.0380.0170.0410.0180.0480.2580.2670.3300.1150.1130.0950.4270.4340.3310.2880.1280.0170.1800.3330.5140.8700.8970.9271.0001.0001.0000.9601.0000.8710.000
NIBRS Crime Against0.0500.0530.0530.0540.0310.0220.0310.0220.0510.0230.6960.7290.0240.0420.0030.1090.2200.1620.0530.0560.0870.0870.0230.0330.0230.0360.0240.0440.4110.1010.3900.0870.0730.0450.4160.6460.3500.3510.1320.0260.3110.2840.3310.9420.9230.7711.0001.0001.0000.9100.8510.6810.003
NIBRS Code0.0740.0700.0750.0710.0560.0200.0540.0200.0850.0200.7900.7270.0380.0530.0000.1490.1420.2200.0570.0540.0610.0610.0180.0370.0180.0390.0190.0460.2570.2670.3370.1200.1140.1100.3070.3400.3230.2830.1000.0190.2180.3400.5230.7510.7370.8850.9960.9600.9101.0001.0000.8900.000
NIBRS Group0.0520.0500.0470.0500.0240.0270.0250.0270.0520.0280.6540.8390.0310.0330.0050.0950.2180.1680.0480.0440.0670.0720.0280.0360.0280.0400.0290.0460.5070.1120.3510.0690.0570.0470.3770.5420.3820.3840.0470.0210.2320.1330.0510.9290.9280.6591.0001.0000.8511.0001.0001.0000.004
NIBRS Type0.0470.0400.0360.0350.0320.0140.0330.0140.0520.0150.4090.5280.0230.0250.0000.0940.1270.0970.0300.0300.0450.0490.0130.0270.0130.0280.0140.0320.2810.0840.2080.1030.0880.0620.2890.3820.2670.2350.0480.0110.1330.1230.1290.7850.7220.7040.8720.8710.6810.8901.0001.0000.000
State0.0070.0120.0110.0820.0050.0020.0050.0010.0080.0010.0000.0060.0040.0080.7590.0000.0090.0070.0610.0090.0220.0530.0020.0020.0020.0020.0020.0010.0470.0000.0000.0000.0000.0000.0000.0150.0090.0050.0180.0000.0030.0100.0000.0080.0020.0060.0000.0000.0030.0000.0040.0001.000

Missing values

2023-04-21T15:40:21.975319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-21T15:40:35.922265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-21T15:41:22.633964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Incident Number w/yearYear of IncidentService Number IDWatchCall (911) ProblemType of IncidentType LocationType of PropertyIncident AddressApartment NumberReporting AreaBeatDivisionSectorCouncil DistrictTarget Area Action GridsCommunityDate1 of OccurrenceYear1 of OccurrenceMonth1 of OccurenceDay1 of the WeekTime1 of OccurrenceDay1 of the YearDate2 of OccurrenceYear2 of OccurrenceMonth2 of OccurenceDay2 of the WeekTime2 of OccurrenceDay2 of the YearDate of ReportDate incident createdOffense Entered YearOffense Entered MonthOffense Entered Day of the WeekOffense Entered TimeOffense Entered Date/TimeCFS NumberCall Received Date TimeCall Date TimeCall Cleared Date TimeCall Dispatch Date TimeSpecial Report (Pre-RMS)Person Involvement TypeVictim TypeVictim RaceVictim EthnicityVictim GenderResponding Officer #1 Badge NoResponding Officer #1 NameResponding Officer #2 Badge NoResponding Officer #2 NameReporting Officer Badge NoAssisting Officer Badge NoReviewing Officer Badge NoElement Number AssignedInvestigating Unit 1Investigating Unit 2Offense StatusUCR DispositionModus Operandi (MO)Family OffenseHate CrimeHate Crime DescriptionWeapon UsedGang Related OffenseDrug Related IstevencidentRMS CodeCriminal Justice Information Service CodePenal CodeUCR Offense NameUCR Offense DescriptionUCR CodeOffense TypeNIBRS CrimeNIBRS Crime CategoryNIBRS Crime AgainstNIBRS CodeNIBRS GroupNIBRS TypeUpdate DateX CoordinateY CordinateZip CodeCityStateLocation1
0203058-20222022203058-2022-011PSE/09 - THEFTTHEFT OF PROP (AUTO ACC) <$100 - (NOT EMP)Apartment Parking LotMotor Vehicle7152 FAIR OAKS AVENaN4527.0217.0NORTHEAST210.0D13Five PointsVickery Meadows_PFA00:00.02022NovemberTue20:0031200:00.02022.0NovemberWed7:00313.003:00.052:49.02022NovemberWed8:5231322-219863103:54.003:54.004:27.037:45.0NaNVictimIndividualHispanic or LatinoHispanic or LatinoFemale94392WILLIS,LINDA,MNaNNaN94392T124554654EX07InvestigationsProperty Crime Division / NE Property CrimesSuspendedSuspendedUNKN SUSP REMOVED THE REAR LICENSE PLATE OFF A NISSANFalseNaNNoneNaNNaNNoMC-99999999-F13399999999PC 31.03(f)NaNNaNNaNNaNTHEFT OF MOTOR VEHICLE PARTS OR ACCESSORIESLARCENY/ THEFT OFFENSESPROPERTY23GACoded15:06.02503598.1137005279.01475231.0DALLASTX7152 FAIR OAKS AVE\nDALLAS, TX 75231\n(32.87309, -96.75785)
1232349-20222022232349-2022-01240/01 - OTHERHARASSMENTOutdoor Area Public/PrivateNaN2525 PLEASANT DRNaN1243.0324.0SOUTHEAST320.0D5NaNNaN00:00.02022DecemberFri15:1636400:00.02022.0DecemberFri15:16364.014:00.054:25.02022DecemberFri17:5436422-253648814:04.014:03.002:08.032:58.0NaNVictimIndividualWhiteNon-Hispanic or LatinoMale9704STUARD,JCNaNNaN97048776117512B342InvestigationsProperty Crime Division / SE Property CrimesOpenOpenSUSP MADE THREATENING PHONE CALL TO COMPFalseNaNNoneNaNUNKNoMB-13160012-T3513160012PC 42.07(c)NaNNaNNaNNaNINTIMIDATIONASSAULT OFFENSESPERSON13CACoded40:28.02528248.7296962802.64375227.0DALLASTX2525 PLEASANT DR\nDALLAS, TX 75227\n(32.75592, -96.67864)
2217269-20222022217269-2022-01111B - BURG OF BUSBURGLARY OF BUILDING - FORCED ENTRYRestaurant/Food Service/TABC LocationNaN10443 N CENTRAL EXPYNaN1052.0653.0NORTH CENTRAL650.0D11NaNNaN00:00.02022DecemberSun0:0033800:00.02022.0DecemberSun8:10338.033:00.047:45.02022DecemberSun8:4733822-236160333:26.033:26.000:26.039:36.0NaNVictimBusinessNaNNaNNaN10170KUSCHEL,ADAM,SCOTTNaNNaN101701073015356B631InvestigationsProperty Crime Division / NC Property CrimesSuspendedSuspendedSUSP PRIED FRONT DOOR, DAMAGE ALARM PANEL, TAMPERED WITH SAFEFalseNaNNoneNaNNaNNoFS-22990001-E122990001PC 30.02(c)(1)NaNNaNNaNNaNBURGLARY-BUSINESSBURGLARY/ BREAKING & ENTERINGPROPERTY220ACoded12:13.02499343.7697010970.95075231.0DALLASTX10443 N CENTRAL EXPY\nDALLAS, TX 75231\n(32.88869, -96.77017)
3232572-20212021232572-2021-0216X - MAJOR DIST (VIOLENCE)PUBLIC INTOXICATIONOutdoor Area Public/PrivateNaN6336 ALCORN AVENaN2208.0352.0SOUTHEAST350.0D8Loop12 JimMillerNaN00:00.02021DecemberMon2:3036100:00.02021.0DecemberMon2:45361.047:00.035:04.02021DecemberMon4:3536121-247055247:00.047:00.034:12.018:03.0NaNVictimSociety/PublicNaNNaNNaN11935DILLARD,FREEMAN,DNaNNaN11935NaN106291A354NaNNaNClear by ArrestCBA (Over Age 17)PUBLIC INTOXICATIONFalseNaNNoneNaNNaNNoMC-99999999-NC31399999999PC 49.02NaNNaNNaNNaNPUBLIC INTOXICATIONPUBLIC INTOXICATIONSOCIETY90ZB90Z - No Coded30:37.02518630.8186948609.33875217.0DALLASTX6336 ALCORN AVE\nDALLAS, TX 75217\n(32.71761, -96.71109)
4223521-20222022223521-2022-013DASF-DIST ACTIVE SHOOTER FOOTDEADLY CONDUCT DISCHARGE FIREARMSingle Family Residence - OccupiedNaN226 LONGBRANCH LNNaN4530.0354.0SOUTHEAST350.0D8Loop12 JimMillerNaN00:00.02022DecemberWed22:2134800:00.02022.0DecemberWed22:21348.021:00.010:26.02022DecemberThu1:1034922-243416321:29.021:29.022:36.023:59.0NaNVictimSociety/PublicNaNNaNNaN9074PULLIAM,JEREMY,DEANNaNNaN907410774106291A314InvestigationsCapers / AssaultsSuspendedSuspendedWITNESS OBSERVED THE SUSPECT FIRE A GUN IN THE AIRFalseNaNNoneHandgunNaNNoF3-52130005-D4852130005PC 22.05(b)NaNNaNNaNNaNWEAPON LAW VIOLATIONSWEAPON LAW VIOLATIONSSOCIETY520ACoded50:50.02525197.6726946246.00875217.0DALLASTX226 LONGBRANCH LN\nDALLAS, TX 75217\n(32.71056, -96.69018)
5225434-20222022225434-2022-01140 - OTHERCRIMINAL TRESPASS WARNINGRestaurant/Food Service/TABC LocationNaN10433 N CENTRAL EXPYNaN1052.0653.0NORTH CENTRAL650.0D11NaNNaN00:00.02022DecemberSun2:1335200:00.02022.0DecemberSun3:22352.022:00.011:16.02022DecemberSun3:1135222-245616012:32.012:32.049:39.043:20.0NaNVictimBusinessNaNNaNNaNNaNNaN10917STINSON,TIMOTHYNaNNaN111210A644NaNNaNSuspendedSuspendedSUSP ENTERED LOCATION WITHOUT CONSENTFalseNaNNoneNaNNaNNoNA-99999999-MSC1199999999No OffenseNaNNaNNaNNaNMISCELLANEOUSMISCELLANEOUSMISCELLANEOUS999C999 - No Coded34:20.02499337.0837010824.40075231.0DALLASTX10433 N CENTRAL EXPY\nDALLAS, TX 75231\n(32.88869, -96.77017)
6177540-20222022177540-2022-02358 - ROUTINE INVESTIGATIONUNLAWFUL CARRYING WEAPONHighway, Street, Alley ETCNaN300 BECKLEYMEADE AVENaN4388.0748.0SOUTH CENTRAL740.0D8NaNNaN00:00.02022SeptemberThu22:0027200:00.02022.0SeptemberThu22:01272.002:00.020:53.02022SeptemberThu23:2027222-191843802:32.002:32.036:35.002:32.0NaNVictimSociety/PublicNaNNaNNaN11862RICKERMAN,RYAN,JACOB11632MARSHALL,AUSTIN,TANNER11862NaN57074E783NaNNaNClear by ArrestCBA (Over Age 17)AP WAS IN POSSESSION OF A HANDGUN WHILE UNDER 21FalseNaNNoneHandgunNaNYesMA-52030027-M2052030027PC 46.02(b)NaNNaNNaNNaNWEAPON LAW VIOLATIONSWEAPON LAW VIOLATIONSSOCIETY520ACoded36:16.02483735.0566920028.72275232.0DALLASTX300 BECKLEYMEADE AVE\nDALLAS, TX 75232\n(32.640391, -96.826874)
7229124-20222022229124-2022-01358 - ROUTINE INVESTIGATIONFOUND PROPERTY (NO OFFENSE)Highway, Street, Alley ETCNaN334 S HALL STNaN2080.0153.0CENTRAL150.0D2Monument GoodLatimerNaN00:00.02022DecemberSat18:3035800:00.02022.0DecemberSat18:40358.041:00.047:03.02022DecemberSat18:4735822-249961341:56.041:56.032:52.041:56.0NaNVictimIndividualBlackNon-Hispanic or LatinoMale12246DURRANT,RYAN9716WOMACK,VALERIE,GAIL12246NaN129123C143NaNNaNSuspendedSuspendedFOUND PROPERTYFalseNaNNoneNaNNaNNoNA-99999999-X399999999No OffenseNaNNaNNaNNaNMISCELLANEOUSMISCELLANEOUSMISCELLANEOUS999C999 - No Coded47:18.02497576.1376972074.12875226.0DALLASTX334 S HALL ST\nDALLAS, TX 75226\n(32.78281, -96.77838)
8045329-20202020045329-2020-01109V - UUMVUNAUTHORIZED USE OF MOTOR VEH - TRUCK OR BUSParking (Business)NaN301 S HARWOOD ST10152074.0134.0CENTRAL130.0D14NaNNaN00:00.02020MarchFri21:006600:00.02020.0MarchSat8:3067.001:00.003:11.02020MarchSat12:036720-043749501:41.001:05.035:30.013:02.0NaNVictimIndividualHispanic or LatinoHispanic or LatinoFemale9397SALGADO,RAMONNaNNaN9397T270123375L236InvestigationsSpecial Investigations / Auto TheftSuspendedSuspendedUNK SUSP TOOK COMP'S VEHICLE WITHOUT CONSENT.FalseNaNNoneNaNNaNNoFS-24110003-G1424110003PC 31.07NaNNaNNaNNaNUUMVMOTOR VEHICLE THEFTPROPERTY240ANot Coded04:27.02492829.5196971020.77675201.0DALLASTX301 S HARWOOD ST\nDALLAS, TX 75201\n(32.78009, -96.79378)
9227537-20222022227537-2022-0127X - MAJOR ACCIDENTOTHER OFFENSE - MISDEMEANORParking (Business)NaN3760 S BUCKNER BLVDNaN1237.0322.0SOUTHEAST320.0D7LakeJune BucknerNaN00:00.02022DecemberWed15:0235500:00.02022.0DecemberWed15:02355.002:00.012:18.02022DecemberWed16:1235522-247876102:45.002:45.006:47.019:10.0NaNVictimSociety/PublicNaNNaNNaN10641SHERMAN,JONATHAN,SCOTT12352RIDDLE,AUSTIN10641NaN15348C337NaNNaNClear by ArrestCBA (Age 17)SUSP ISSUED CITATION FOR DWLI AND OPEN CONTAINERFalseNaNNoneNaNNaNNoM*-99999999-U14299999999New or Missing Misd CodesNaNNaNNaNNaNALL OTHER OFFENSESALL OTHER OFFENSESPERSON, PROPERTY, OR SOCIETY90ZB90Z - No Coded52:59.02527186.4466967515.68375227.0DALLASTX3760 S BUCKNER BLVD\nDALLAS, TX 75227\n(32.76893, -96.68259)
Incident Number w/yearYear of IncidentService Number IDWatchCall (911) ProblemType of IncidentType LocationType of PropertyIncident AddressApartment NumberReporting AreaBeatDivisionSectorCouncil DistrictTarget Area Action GridsCommunityDate1 of OccurrenceYear1 of OccurrenceMonth1 of OccurenceDay1 of the WeekTime1 of OccurrenceDay1 of the YearDate2 of OccurrenceYear2 of OccurrenceMonth2 of OccurenceDay2 of the WeekTime2 of OccurrenceDay2 of the YearDate of ReportDate incident createdOffense Entered YearOffense Entered MonthOffense Entered Day of the WeekOffense Entered TimeOffense Entered Date/TimeCFS NumberCall Received Date TimeCall Date TimeCall Cleared Date TimeCall Dispatch Date TimeSpecial Report (Pre-RMS)Person Involvement TypeVictim TypeVictim RaceVictim EthnicityVictim GenderResponding Officer #1 Badge NoResponding Officer #1 NameResponding Officer #2 Badge NoResponding Officer #2 NameReporting Officer Badge NoAssisting Officer Badge NoReviewing Officer Badge NoElement Number AssignedInvestigating Unit 1Investigating Unit 2Offense StatusUCR DispositionModus Operandi (MO)Family OffenseHate CrimeHate Crime DescriptionWeapon UsedGang Related OffenseDrug Related IstevencidentRMS CodeCriminal Justice Information Service CodePenal CodeUCR Offense NameUCR Offense DescriptionUCR CodeOffense TypeNIBRS CrimeNIBRS Crime CategoryNIBRS Crime AgainstNIBRS CodeNIBRS GroupNIBRS TypeUpdate DateX CoordinateY CordinateZip CodeCityStateLocation1
1038493116504-20192019116504-2019-01224 - ABANDONED PROPERTYUNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILEApartment Parking LotMotor Vehicle8039 CHARIOT DR20181217.0318.0SOUTHEAST310.0D7Buckner 30NaN00:00.02019JuneTue2:0015500:00.02019.0JuneSun4:00160.049:00.007:01.02019JuneMon10:0716119-105208441:06.039:46.017:15.059:39.0NaNVictimIndividualHispanic or LatinoHispanic or LatinoFemale11502FLANAGAN-YOSHIDA,JESSICA,DELAYNE8763CHAVARRIA,SERGIO,LUIS1150210197120627D356InvestigationsSpecial Investigations / Auto TheftSuspendedSuspendedUNK SUSP TOOK COMPS VEH AND RETURNED BACK TO LOCATION.FalseNaNNoneNaNNaNNoFS-24110003-G1324110003PC 31.07NaNNaNNaNNaNUUMVMOTOR VEHICLE THEFTPROPERTY240ANot Coded32:22.02526178.6826972155.79475227.0DALLASTX8039 CHARIOT DR\nDALLAS, TX 75227\n(32.782058, -96.685159)
1038494129101-20212021129101-2021-01111R - BURG OF RESBURGLARY OF HABITATION -NO FORCED ENTRYApartment Complex/BuildingNaN4606 CEDAR SPRINGS RD17243104.0542.0NORTHWEST540.0D2Wycliff LemmonNaN00:00.02021JulyMon21:0020000:00.02021.0JulyTue13:00201.024:00.017:04.02021JulyTue14:1720121-135153936:10.036:10.050:00.059:47.0NaNVictimIndividualBlackNon-Hispanic or LatinoFemale8127BRYANT,KEVIN,JAMESNaNNaN8127T16170495B552InvestigationsProperty Crime Division / NW Property CrimesSuspendedSuspendedUNK SUSP ENTERED LOC AND TOOK PROPFalseNaNNoneNaNNaNNoF2-22990002-E622990002PC 30.02(c)(2)NaNNaNNaNNaNBURGLARY-RESIDENCEBURGLARY/ BREAKING & ENTERINGPROPERTY220ACoded18:03.02485048.3046985032.97475219.0DALLASTX4606 CEDAR SPRINGS RD\nDALLAS, TX 75219\n(32.817776, -96.819571)
1038495079916-20192019079916-2019-01107 - MINOR ACCIDENTCRIM MISCHIEF >OR EQUAL $750 < $2,500Apartment Complex/BuildingNaN9760 SCYENE RD15121246.0326.0SOUTHEAST320.0D7StAugustine BrutonNaN00:00.02019AprilSun20:0011100:00.02019.0AprilMon16:00112.008:00.058:25.02019AprilMon19:5811219-071048508:20.008:20.005:56.023:28.0NaNVictimIndividualBlackNon-Hispanic or LatinoFemale10988MULRENAN,SEAN,MNaNNaN10988NaN36201C331NaNNaNSuspendedSuspendedUNKNOWN SUSP DAMAGED COMPS VEHFalseNaNNoneNaNNaNNoMA-29990043-L10029990043PC 28.03(b)(3)(A)NaNNaNNaNNaNDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYDESTRUCTION/ DAMAGE/ VANDALISM OF PROPERTYPROPERTY290ACoded43:03.02535905.2476964737.30675227.0DALLASTX9760 SCYENE RD\nDALLAS, TX 75227\n(32.762243, -96.653982)
1038496102371-20212021102371-2021-01358 - ROUTINE INVESTIGATIONUNAUTHORIZED USE OF MOTOR VEH - AUTOMOBILEShopping MallNaN9751 WALNUT STNaN9602.0254.0NORTHEAST250.0D10NaNNaN00:00.02021JuneWed19:5016000:00.02021.0JuneWed19:52160.000:00.007:15.02021JuneThu9:0716121-104685600:17.000:17.019:15.000:17.0NaNNaNNaNNaNNaNNaN8353FINNEY,ALICIA,MARIANaNNaN8353T168111210B692InvestigationsSpecial Investigations / Auto TheftSuspendedSuspendedUNKNOWN SUSP REMOVED VICTIM'S VEH W/OUT PERMISSIONFalseNaNNoneNaNNaNNoFS-24110003-G1324110003PC 31.07NaNNaNNaNNaNUUMVMOTOR VEHICLE THEFTPROPERTY240ACoded23:59.02514985.1617023656.76075243.0DALLASTX9751 WALNUT ST\nDALLAS, TX 75243\n(32.923547, -96.71924)
1038497805903-20202020805903-2020-012NaNFRAUD USE/POSS IDENTIFYING INFO-PRELIMINARY INVESTIGATIONBusiness OfficeNaN6917 PICKRELL DRNaN1217.0318.0SOUTHEAST310.0D7NaNNaN00:00.02020MayTue13:0013300:00.02020.0MayFri13:30143.052:41.000:13.02020MayFri14:00143NaNNaNNaNNaNNaNNaNVictimIndividualHispanic or LatinoHispanic or LatinoMaleNaNNaNNaNNaNNaNT255106845NaNInvestigationsSpecial Investigations / Financial CrimesClosed/ClearedClosedNaNNaNNaNNoneNaNNoNaNNA-99999999-Z2899999999Fraud InvestigationNaNNaNNaNNaNMISCELLANEOUSMISCELLANEOUSMISCELLANEOUS999C999 - No Coded22:21.02523633.1926975077.79275227.0DALLASTX6917 PICKRELL DR\nDALLAS, TX 75227\n(32.789644, -96.69339)
1038498132431-20202020132431-2020-01355 - TRAFFIC STOPPOSS MARIJUANA >4OZ< OR EQUAL 5LBSHighway, Street, Alley ETCNaN2998 PEABODY AVENaN2129.0343.0SOUTHEAST340.0D7JuliusSchepps CentralMLK_PFA00:00.02020JulyTue22:2021000:00.02020.0JulyTue22:20210.021:00.055:55.02020JulyTue22:5521020-136026019:59.019:59.026:15.020:01.0NaNVictimGovernmentNaNNaNNaN11015DYE,BRANDON,MARKIESTNaNNaN11015NaN122184Y323NaNNaNClear by ArrestCBA (Over Age 17)AP WAS FOUND TO BE IN POSSESSION OF MARIJUANAFalseNaNNoneNaNNaNNoFS-35620010-O335620010HSC 481.121(b)(3)NaNNaNNaNNaNDRUG/ NARCOTIC VIOLATIONSDRUG/ NARCOTIC VIOLATIONSSOCIETY35AACoded03:21.02501135.5156966969.43075215.0DALLASTX2998 PEABODY AVE\nDALLAS, TX 75215\n(32.769193, -96.766376)
1038499062055-20182018062055-2018-01307 - MINOR ACCIDENTFOUND PROPERTY (NO OFFENSE)Highway, Street, Alley ETCNaN7000 BRIERFIELD DRNaN4350.0743.0SOUTH CENTRAL740.0D3NaNNaN00:00.02018MarchSat16:448300:00.02018.0MarchSat16:5383.054:00.010:00.02018MarchSat15:108318-051505153:08.053:08.019:57.043:49.0NaNVictimGovernmentNaNNaNNaN11107BELL,ROBERT,ASHLEENaNNaN11107NaN121171C745NaNNaNSuspendedSuspendedFOUND PROPERTYFalseNaNNoneNaNNaNNoNA-99999999-X399999999No OffenseFOUNDFOUND PROPERTY4300.0NOT CODEDMISCELLANEOUSMISCELLANEOUSMISCELLANEOUS999C999 - No Coded35:42.02483379.1186928447.62675232.0DALLASTX7000 BRIERFIELD DR\nDALLAS, TX 75232\n(32.663261, -96.826813)
1038500180588-20212021180588-2021-0137XF - MAJOR ACCIDENT FREEWAYFOUND PROPERTY (NO OFFENSE)Outdoor Area Public/PrivateNaN4151 S FITZHUGH AVENaN2099.0115.0CENTRAL110.0D7NaNNaN00:00.02021OctoberMon22:2327700:00.02021.0OctoberMon22:29277.001:00.057:25.02021OctoberMon23:5727721-189725216:07.016:07.033:03.023:48.0NaNVictimIndividualBlackNon-Hispanic or LatinoMale12047RAMIREZ,NANCY11982MAZZA,MARIO12047NaN120430C131NaNNaNSuspendedSuspendedPROPERTY LEFT IN VEHICLEFalseNaNNoneNaNNaNNoNA-99999999-X399999999No OffenseNaNNaNNaNNaNMISCELLANEOUSMISCELLANEOUSMISCELLANEOUS999C999 - No Coded20:51.02505485.1086969876.75875210.0DALLASTX4151 S FITZHUGH AVE\nDALLAS, TX 75210\n(32.776398, -96.752685)
1038501055916-20182018055916-2018-01311V - BURG MOTOR VEHBMVHighway, Street, Alley ETCNaN7731 CLAREMONT DRNaN1200.0237.0NORTHEAST230.0D7NaNNaN00:00.02018MarchFri17:007500:00.02018.0MarchFri18:0075.000:00.058:49.02018MarchFri19:587518-046354000:51.000:08.014:38.029:54.0NaNVictimIndividualHispanic or LatinoHispanic or LatinoMale10544TOWNSON,RICHARDNaNNaN10544T269121171C213InvestigationsProperty Crime Division / NE Property CrimesSuspendedSuspendedCOMP'S PROPERTY WAS TAKEN FROM VEH BY UNK SUSPECT.FalseNaNNoneNaNNaNNoMA-22990004-F122990004PC 30.04(a)THEFT/BMVTHEFT640.0PART1THEFT FROM MOTOR VEHICLELARCENY/ THEFT OFFENSESPROPERTY23FACoded25:09.02517889.6996976950.06675228.0DALLASTX7731 CLAREMONT DR\nDALLAS, TX 75228\n(32.79569, -96.712134)
1038502173129-20212021173129-2021-01358 - ROUTINE INVESTIGATIONPOSSESSION OF DRUG PARAPHERNALIAHighway, Street, Alley ETCNaN1100 E 10TH STNaN4123.0711.0SOUTH CENTRAL710.0D4NaNNaN00:00.02021SeptemberThu22:0026600:00.02021.0SeptemberThu22:05266.006:00.027:38.02021SeptemberThu22:2726621-181603357:36.057:36.025:58.057:36.0Alan Ross Texas Freedom ParadeVictimSociety/PublicNaNNaNNaN11602FARMER,JAMIE,COREYNaNNaN11602NaN57074E772NaNNaNClear by ArrestCBA (Over Age 17)AP POSSESSED GLASS SMOKING PIPE AND SYRINGE.FalseNaNNoneNaNNaNYesMC-35500015-O16135500015HSC 481.125 (A)NaNNaNNaNNaNDRUG EQUIPMENT VIOLATIONSDRUG/ NARCOTIC VIOLATIONSSOCIETY35BACoded04:13.02488556.0176959474.72475203.0DALLASTX1100 E 10TH ST\nDALLAS, TX 75203\n(32.748672, -96.808092)